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Multimodal Human Computer Interaction and Pervasive Services Patrizia Grifoni Istituto di Ricerche sulla Popolazione e le Politiche Sociali Consiglio Nazionale delle Ricerche, Italy

InformatIon scIence reference Hershey • New York

Director of Editorial Content: Senior Managing Editor: Managing Editor: Assistant Managing Editor: Typesetter: Cover Design: Printed at:

Kristin Klinger Jamie Snavely Jeff Ash Carole Coulson Carole Coulson Lisa Tosheff Yurchak Printing Inc.

Published in the United States of America by Information Science Reference (an imprint of IGI Global) 701 E. Chocolate Avenue, Hershey PA 17033 Tel: 717-533-8845 Fax: 717-533-8661 E-mail: [email protected] Web site: http://www.igi-global.com/reference and in the United Kingdom by Information Science Reference (an imprint of IGI Global) 3 Henrietta Street Covent Garden London WC2E 8LU Tel: 44 20 7240 0856 Fax: 44 20 7379 0609 Web site: http://www.eurospanbookstore.com Copyright © 2009 by IGI Global. All rights reserved. No part of this publication may be reproduced, stored or distributed in any form or by any means, electronic or mechanical, including photocopying, without written permission from the publisher. Product or company names used in this set are for identification purposes only. Inclusion of the names of the products or companies does not indicate a claim of ownership by IGI Global of the trademark or registered trademark. Library of Congress Cataloging-in-Publication Data Multimodal human computer interaction and pervasive services / Patrizia Grifoni, editor. p. cm. Includes bibliographical references and index. Summary: "This book provides concepts, methodologies, and applications used to design and develop multimodal systems"--Provided by publisher. ISBN 978-1-60566-386-9 (hardcover) -- ISBN 978-1-60566-387-6 (ebook) 1. Human-computer interaction. 2. Interactive multimedia. 3. Ubiquitous computing. I. Grifoni, Patrizia, 1959QA76.9.H85M853 2009 004.01'9--dc22 2008055308 British Cataloguing in Publication Data A Cataloguing in Publication record for this book is available from the British Library. All work contributed to this book is new, previously-unpublished material. The views expressed in this book are those of the authors, but not necessarily of the publisher.

Editorial Advisory Board Augusto Celentano, Università Ca’ Foscari di Venezia, Italy Fang Chen, National ICT, Australia Deborah Dahl, Conversational Technologies, USA Fernando Ferri, Istituto di Ricerche sulla Popolazione e le Politiche Sociali -Consiglio Nazionale delle Ricerche, Italy Irina Kondratova, NRC Institute for Information Technology-National Research Council, Canada

List of Reviewers Augusto Celentano, Università Ca’ Foscari di Venezia, Italy Fang Chen, National ICT, Australia Karin Coninx, Hasselt University, Belgium Deborah Dahl, Conversational Technologies, USA Fernando Ferri, Istituto di Ricerche sulla Popolazione e le Politiche Sociali -Consiglio Nazionale delle Ricerche, Italy Irina Kondratova, NRC Institute for Information Technology-National Research Council, Canada Marco Padula, Istituto di Tecnologia delle Costruzioni -Consiglio Nazionale delle Ricerche, Italy Domenico M. Pisanelli, Istituto di Scienze e Tecnologie della Cognizione-Consiglio Nazionale delle Ricerche, Italy Fabio Pittarello, Università Ca’ Foscari di Venezia, Italy Maurizio Rafanelli, Istituto di Analisi dei Sistemi ed Informatica “Antonio Ruberti”-Consiglio Nazionale delle Ricerche, Italy

Table of Contents

Preface ............................................................................................................................................... xvii Acknowledgment .............................................................................................................................. xxii Section I Multimodality: Basic Concepts Chapter I A Qualitative Study of the Applicability of Technology Acceptance Models to Senior Mobile Phone Users ................................................................................................................................ 1 Judy van Biljon, University of South Africa, South Africa Karen Renaud, University of Glasgow, UK Chapter II Analyzing Multimodal Interaction........................................................................................................ 19 Fernando Ferri, Istituto di Ricerche sulla Popolazione e le Politiche Sociali-Consiglio Nazionale delle Ricerche, Italy Stefano Paolozzi, Istituto di Ricerche sulla Popolazione e le Politiche Sociali-Consiglio Nazionale delle Ricerche, Italy Chapter III Exploring Multimodal Input Fusion Strategies .................................................................................... 34 Arianna D’Ulizia, Istituto di Ricerche sulla Popolazione e le Politiche Sociali-Consiglio Nazionale delle Ricerche, Italy Chapter IV An Efficient Unification-Based Multimodal Language Processor for Multimodal Input Fusion ......... 58 Fang Chen, National ICT Australia, Australia & University of Sydney, Australia Yong Sun, National ICT Australia, Australia & University of Sydney, Australia

Chapter V Interpretation Methods and Ambiguity Management in Multimodal Systems .................................... 87 Maria Chiara Caschera, Istituto di Ricerche sulla Popolazione e le Politiche Sociali-Consiglio Nazionale delle Ricerche, Italy Chapter VI Multimodal Fission ............................................................................................................................. 103 Patrizia Grifoni, Istituto di Ricerche sulla Popolazione e le Politiche Sociali-Consiglio Nazionale delle Ricerche, Italy Chapter VII Machine Learning Enhancing Adaptivity of Multimodal Mobile Systems ........................................ 121 Floriana Esposito, Università di Bari, Italy Teresa M. A. Basile, Università di Bari, Italy Nicola Di Mauro, Università di Bari, Italy Stefano Ferilli, Università di Bari, Italy Chapter VIII Model-Based Design for Multimodal Interaction in a VE.................................................................. 139 Karin Coninx, Hasselt University, Belgium Joan De Boeck, Hasselt University, Belgium Chris Raymaekers, Hasselt University, Belgium Lode Vanacken, Hasselt University, Belgium Chapter IX Temporal and Spatial Aspects of Pointing Gestures ........................................................................... 166 Christian Müller-Tomfelde, CSIRO ICT-Centre, Australia Fang Chen, National ICT Australia, Australia Section II From Unimodal to Multimodal Interaction: Applications and Services Chapter X Speech and Gaze Control for Desktop Environments ........................................................................ 188 Emiliano Castellina, Politecnico di Torino, Italy Fulvio Corno, Politecnico di Torino, Italy Paolo Pellegrino, Politecnico di Torino, Italy Chapter XI Multimodality and Environment Semantics ....................................................................................... 204 Fabio Pittarello, Università Ca’ Foscari Venezia, Italy Augusto Cementano, Università Ca’ Foscari Venezia, Italy

Chapter XII An Ontology Supporting an On-Board Vehicle Multimodal Interaction System ............................... 230 Domenico M. Pisanelli, CNR-ISTC, Italy Claudio De Lazzari, CNR IFC, Italy Emilio Bugli Innocenti, Netxcalibur, Italy Norma Zanetti, Netxcalibur, Italy Chapter XIII Designing Pervasive and Multimodal Interactive Systems: An Approach Built on the Field ............ 243 Barbara R. Barricelli, Università degli Studi di Milano, Italy Andrea Marcante, Università degli Studi di Milano, Italy Piero Mussio, Università degli Studi di Milano, Italy Loredana Parasiliti Provenza, Università degli Studi di Milano, Italy Marco Padula, Istituto per le Tecnologie della Costruzione – Consiglio Nazionale delle Ricerche, Italy Paolo L. Scala, Istituto per le Tecnologie della Costruzione – Consiglio Nazionale delle Ricerche, Italy Chapter XIV Making the Web Accessible to the Visually Impaired ........................................................................ 265 Simone Bacellar Leal Ferriera, Universidade Federal do Estado do Rio de Janeiro, Brazil Denis Silva da Silveira, Programa de Engenharia de Produção - COPPE/UFRJ, Brazil Marcos Gurgel do Amaral Leal Ferreira, Holden Comunicação Ltda, Brazil Ricardo Rodrigues Nunes, Universidade Federal do Estado do Rio de Janeiro, Brazil Chapter XV Designing Contextualized Interaction for Learning ........................................................................... 278 Marcus Specht, Open University of the Netherlands, The Netherlands Section III Multimodal Interaction in Mobile Environment Chapter XVI Speech Driven Interaction in Mobile Multimodality .......................................................................... 293 Giovanni Frattini, Engineering IT, Italy Fabio Corvino, Engineering IT, Italy Francesco Gaudino, Engineering IT, Italy Pierpaolo Petriccione, Engineering IT, Italy Vladimiro Scotto di Carlo, Engineering IT, Italy Gianluca Supino, Engineering IT, Italy

Chapter XVII Providing Mobile Multimodal Social Services Using a Grid Architecture ........................................ 315 Stefania Pierno, Engineering IT, Italy Vladimiro Scotto di Carlo, Engineering IT, Italy Massimo Magaldi, Engineering IT, Italy Roberto Russo, Engineering IT, Italy Gian Luca Supino, Engineering IT, Italy Luigi Romano, Engineering IT, Italy Luca Bevilacqua, Engineering IT, Italy Chapter XVIII Benefits, Challenges, and Research in Multimodal Mobile GIS ........................................................ 331 Julie Doyle, University College Dublin, Ireland Michela Bertolotto, University College Dublin, Ireland David Wilson, University of North Carolina at Charlotte, USA Chapter XIX Adapted Multimodal End-User Interfaces for XML-Content ............................................................ 351 Benoît Encelle, University Claude Bernard - Lyon 1, LIRIS, France Nadine Baptiste-Jessel, University Paul Sabatier, France Florence Sèdes, University Paul Sabatier, France Chapter XX Mobile Virtual Blackboard as Multimodal User Interface.................................................................. 366 Sladjana Tesanovic, GISDATA Skopje, Macedonia Danco Davcev, University “Ss Cyril and Methodius”, Macedonia Vladimir Trajkovik, University “Ss Cyril and Methodius”, Macedonia Chapter XXI Mobile Devices to Support Advanced Forms of E-Learning.............................................................. 389 Alessia D’Andrea, Istituto di Ricerche sulla Popolazione e le Politiche Sociali-Consiglio Nazionale delle Ricerche, Italy Fernando Ferri, Istituto di Ricerche sulla Popolazione e le Politiche Sociali-Consiglio Nazionale delle Ricerche, Italy Leopoldina Fortunati, Università degli Studi di Udine, Italy Tiziana Guzzo, Istituto di Ricerche sulla Popolazione e le Politiche Sociali-Consiglio Nazionale delle Ricerche, Italy Section IV Standards, Guidelines, and Evaluation of Multimodal Systems Chapter XXII Standards for Multimodal Interaction ................................................................................................. 409 Deborah A. Dahl, Conversational Technologies, USA

Chapter XXIII Visualising Interactions on Mobile Multimodal Systems ................................................................... 430 Kristine Deray, University of Technology, Sydney, Australia Simeon Simoff, University of Western Sydney, Australia Chapter XXIV Usability Evaluation of Multimodal Interfaces .................................................................................. 443 Regina Bernhaupt, IRIT, IHCS, Toulouse, France Compilation of References ............................................................................................................... 456 About the Contributors .................................................................................................................... 495 Index ................................................................................................................................................... 507

Detailed Table of Contents

Preface ............................................................................................................................................... xvii Acknowledgment .............................................................................................................................. xxii Section I Multimodality: Basic Concepts The first section introduces some of the basic concepts and describes the main theoretical and practical problems and features of multimodal interaction, with a particular focus on mobile devices. Starting from a study on people acceptance and adoption of technologies and models, with a particular focus on mobile devices in the first chapter, this section has been highlighted how naturalness of multimodal interaction facilitates its acceptance and its wide use diffusion by a large part of people. From Chapter II to Chapter IX the basic concepts, theories, and technologies involved in the study of multimodal interaction and multimodal systems are provided. Chapter I A Qualitative Study of the Applicability of Technology Acceptance Models to Senior Mobile Phone Users ................................................................................................................................ 1 Judy van Biljon, University of South Africa, South Africa Karen Renaud, University of Glasgow, UK This chapter, investigating into mobile phone adoption by elderly people, highlights how acceptance is a preliminary step respect to the technology adoption. In particular, the authors propose and describe the novel senior technology acceptance and adoption model (STAM), which incorporates acceptance factors into the adoption process. Chapter II Analyzing Multimodal Interaction........................................................................................................ 19 Fernando Ferri, Istituto di Ricerche sulla Popolazione e le Politiche Sociali-Consiglio Nazionale delle Ricerche, Italy Stefano Paolozzi, Istituto di Ricerche sulla Popolazione e le Politiche Sociali-Consiglio Nazionale delle Ricerche, Italy

This chapter gives some definitions and basic concepts on multimodality, and describes the different approaches used in the multimodal interaction. In particular, approaches that use new semantic technologies and technologies connected with the Semantic Web fields are discussed. Chapter III Exploring Multimodal Input Fusion Strategies .................................................................................... 34 Arianna D’Ulizia, Istituto di Ricerche sulla Popolazione e le Politiche Sociali-Consiglio Nazionale delle Ricerche, Italy This chapter describes the different approaches that the literature proposes to combine different input for multimodal systems (fusion process), their features, advantages, and di-advantages. Chapter IV An Efficient Unification-Based Multimodal Language Processor for Multimodal Input Fusion ......... 58 Fang Chen, National ICT Australia, Australia & University of Sydney, Australia Yong Sun, National ICT Australia, Australia & University of Sydney, Australia This chapter deals with presenting an approach to multimodal input fusion based on powerful combinational categorical grammar. The proposed approach was validated through users’ experiments demonstrating its usefulness for mobile context according to its low computational costs. Chapter V Interpretation Methods and Ambiguity Management in Multimodal Systems .................................... 87 Maria Chiara Caschera, Istituto di Ricerche sulla Popolazione e le Politiche Sociali-Consiglio Nazionale delle Ricerche, Italy This chapter, starting form considering that more natural the interaction is more ambiguous can be its interpretation, describes the different approaches proposed by the literature in the interpretation process of multimodal input. Moreover, this chapter provides a classification of multimodal ambiguities. Finally, methods used for solving the different classes of ambiguities are presented. Chapter VI Multimodal Fission ............................................................................................................................. 103 Patrizia Grifoni, Istituto di Ricerche sulla Popolazione e le Politiche Sociali-Consiglio Nazionale delle Ricerche, Italy This chapter deals with the information output arrangement and organization (multimodal fission). Considering information structure as the basic concept, the chapter provides some basic notions and describes the most relevant approaches discussed in the literature for designing outputs using different kinds of modality and synchronizing them.

Chapter VII Machine Learning Enhancing Adaptivity of Multimodal Mobile Systems ........................................ 121 Floriana Esposito, Università di Bari, Italy Teresa M. A. Basile, Università di Bari, Italy Nicola Di Mauro, Università di Bari, Italy Stefano Ferilli, Università di Bari, Italy This chapter discusses the importance and features of intelligent interaction stimulated by the wide use of mobile devices for providing new services. In particular, the chapter deals with modeling for enhancing flexibility and adaptability of multimodal systems. In particular, the described machine learning approach proposes an initial static user model based on stereotypes; then, the model is revised/adjusted by considering the user interaction with the device/context. Chapter VIII Model-Based Design for Multimodal Interaction in a VE.................................................................. 139 Karin Coninx, Hasselt University, Belgium Joan De Boeck, Hasselt University, Belgium Chris Raymaekers, Hasselt University, Belgium Lode Vanacken, Hasselt University, Belgium This chapter, starting from the assumption that designing and implementing interaction is a critical issue, proposes a model-based user interface design process (MBUID), in which a multimodal interface can be defined by means of a high abstraction level. The proposed approach permits to generate prototypes that can be evaluated in the intermediate phases of the system development. Chapter IX Temporal and Spatial Aspects of Pointing Gestures ........................................................................... 166 Christian Müller-Tomfelde, CSIRO ICT-Centre, Australia Fang Chen, National ICT Australia, Australia This chapter addresses the concrete aspect of the temporal and spatial organization of human pointing actions for multimodal human computer interaction. Indeed, the organisation of the human pointing gestures in time and space is an essential aspect when using hand-free interaction techniques. The authors provide readers with a substantial knowledge about the temporal and spatial organization of the pointing gesture. Section II From Unimodal to Multimodal Interaction: Applications and Services The second section of this book presents chapters describing problems faced when designing, implementing, and using multimodal systems. These problems and their solutions are discussed considering specific modal and multimodal applications. An example of solution for ambiguities is provided

combining auditive and visual modalities; the problem of accessing objects and services supplied by multimodal mobile applications is faced by multimodal systems in different scenarios using ontologies permitting interoperability between different environments and semantic interoperability for cooperation. The collaborativeness in designing interactive systems is discussed as a relevant issue to handle specific problems and face them on the field. Finally, the importance to make information and services accessible is highlighted, and one adopted solution for visually impaired people is described. Chapter X Speech and Gaze Control for Desktop Environments ........................................................................ 188 Emiliano Castellina, Politecnico di Torino, Italy Fulvio Corno, Politecnico di Torino, Italy Paolo Pellegrino, Politecnico di Torino, Italy This chapter discusses the eye tracking and speech recognition technologies and describes a multimodal system that integrates the two involved input channels. It describes how the combined use of speech and gaze permits to achieve mutual disambiguation. Chapter XI Multimodality and Environment Semantics ....................................................................................... 204 Fabio Pittarello, Università Ca’ Foscari Venezia, Italy Augusto Cementano, Università Ca’ Foscari Venezia, Italy This chapter, starting from the critic aspect of accessing objects and services in a complex environment by users, introduces a methodology to design a system usable and able to help a user to access these objects and services. In particular, this methodology is based on the identification of the user-oriented features of the environment and on their mapping onto a virtual world. Chapter XII An Ontology Supporting an On-Board Vehicle Multimodal Interaction System ............................... 230 Domenico M. Pisanelli, CNR-ISTC, Italy Claudio De Lazzari, CNR IFC, Italy Emilio Bugli Innocenti, Netxcalibur, Italy Norma Zanetti, Netxcalibur, Italy This chapter discusses the importance of ontologies for multimodality describing an on-board vehicle multimodal interaction system. It introduces a modular ontology that, enabling semantic interoperability devoted to allow cooperation between the road infrastructure and drivers, supports the on-board vehicle multimodal interaction.

Chapter XIII Designing Pervasive and Multimodal Interactive Systems: An Approach Built on the Field ............ 243 Barbara R. Barricelli, Università degli Studi di Milano, Italy Andrea Marcante, Università degli Studi di Milano, Italy Piero Mussio, Università degli Studi di Milano, Italy Loredana Parasiliti Provenza, Università degli Studi di Milano, Italy Marco Padula, Istituto per le Tecnologie della Costruzione – Consiglio Nazionale delle Ricerche, Italy Paolo L. Scala, Istituto per le Tecnologie della Costruzione – Consiglio Nazionale delle Ricerche, Italy This chapter presents a methodology for collaborative and evolutionary design of interactive systems. This methodology is derived from empirical real case experiences. The described software environment is discussed emphasising the pervasiveness and usability features connected with multimodality of interaction and portability of the system, and permitting to coordinate desktop and mobile devices use. Chapter XIV Making the Web Accessible to the Visually Impaired ........................................................................ 265 Simone Bacellar Leal Ferriera, Universidade Federal do Estado do Rio de Janeiro, Brazil Denis Silva da Silveira, Programa de Engenharia de Produção - COPPE/UFRJ, Brazil Marcos Gurgel do Amaral Leal Ferreira, Holden Comunicação Ltda, Brazil Ricardo Rodrigues Nunes, Universidade Federal do Estado do Rio de Janeiro, Brazil This chapter deals with providing usability and accessibility features according to the W3C directives focusing on interaction between visually impaired people and Internet. Moreover, it is presented a study on the field carried out in Brazil, with the purpose to verify and integrate the knowledge acquired by the literature about the different types of impositions and limits that these users can have. Chapter XV Designing Contextualized Interaction for Learning ........................................................................... 278 Marcus Specht, Open University of the Netherlands, The Netherlands This chapter describes the experiences of designing mobile and pervasive learning carried out in the European project RAFT. A flexible and powerful approach provided by a widget based design for the user interface is presented; this approach enables the use of mobile phones, PDAs, tablet PCs, desktop computers, as well as electronic whiteboard. Section III Multimodal Interaction in Mobile Environment The third section of this book discusses of multimodal interaction in mobile environment, according to the devices limitations and the different contexts where people can use them. Some architectural solutions, adaptation methods, and uses to the different contexts are presented. A particular attention was devoted to multimodal interfaces for mobile GIS, due the implicit nature of their spatial and temporal

information, the involvement of visual channel, and their tendency to be used in different context by different users. Finally, another emerging scenario discussed by chapters contained in this section deals with the use of multimodal mobile systems for social applications such as e-learning; in particular, specific methods to make adaptable a mobile-learning system and addressing how mobile multimodal interaction is changing many individual and social activities are described. Chapter XVI Speech Driven Interaction in Mobile Multimodality .......................................................................... 293 Giovanni Frattini, Engineering IT, Italy Fabio Corvino, Engineering IT, Italy Francesco Gaudino, Engineering IT, Italy Pierpaolo Petriccione, Engineering IT, Italy Vladimiro Scotto di Carlo, Engineering IT, Italy Gianluca Supino, Engineering IT, Italy This chapter describes an architecture for mobile multimodal applications used to conveniently manage inputs and outputs in order to make the dialog natural, intuitive, and user centred, according to the needs of ubiquitous and context aware applications. The advantages in using a prevalent speech driven interaction, mainly in mobile contexts, when hands or other body parts of users can be busy are underlined. Chapter XVII Providing Mobile Multimodal Social Services Using a Grid Architecture ........................................ 315 Stefania Pierno, Engineering IT, Italy Vladimiro Scotto di Carlo, Engineering IT, Italy Massimo Magaldi, Engineering IT, Italy Roberto Russo, Engineering IT, Italy Gian Luca Supino, Engineering IT, Italy Luigi Romano, Engineering IT, Italy Luca Bevilacqua, Engineering IT, Italy This chapter describes a platform, based on a grid approach, used to develop multichannel, multimodal, and mobile context aware applications. It describes problems and solutions using a scenario about emergencies and crisis management. The platform, however, has a general validity and allows to supply services by a multimodal user interface; the GRID approach enables to optimise the computing load due to the multiple input channels processing. Chapter XVIII Benefits, Challenges, and Research in Multimodal Mobile GIS ........................................................ 331 Julie Doyle, University College Dublin, Ireland Michela Bertolotto, University College Dublin, Ireland David Wilson, University of North Carolina at Charlotte, USA This chapter, starting from the assumption that the user’s interface is a critical aspect of interaction systems, discusses how to design and develop multimodal interfaces for mobile GIS. It describes in particular the multimodal mobile GIS CoMPASS, which enables users to interact by speech and gestures.

Chapter XIX Adapted Multimodal End-User Interfaces for XML-Content ............................................................ 351 Benoît Encelle, University Claude Bernard - Lyon 1, LIRIS, France Nadine Baptiste-Jessel, University Paul Sabatier, France Florence Sèdes, University Paul Sabatier, France This chapter faces one of the most relevant challenges arising with the wide use of Web and mobile devices: making contents accessible personalizing interfaces for highly mobile individuals, for people with limited devices, for people with disabilities, and so on. A personalized multimodal user interface for browsing XML content types is presented as an example of adaptive and natural interaction. Chapter XX Mobile Virtual Blackboard as Multimodal User Interface.................................................................. 366 Sladjana Tesanovic, GISDATA Skopje, Macedonia Danco Davcev, University “Ss Cyril and Methodius”, Macedonia Vladimir Trajkovik, University “Ss Cyril and Methodius”, Macedonia This chapter describes the multimodal mobile virtual blackboard system, a mobile learning system allowing consultation among students and professors. The focus of the description is on the multimodal solution of the user’s interface for the context aware learning system. The chapter, describing the system, suggests good practices to follow in designing and implementing mobile multimodal interfaces. Chapter XXI Mobile Devices to Support Advanced Forms of E-Learning.............................................................. 389 Alessia D’Andrea, Istituto di Ricerche sulla Popolazione e le Politiche Sociali-Consiglio Nazionale delle Ricerche, Italy Fernando Ferri, Istituto di Ricerche sulla Popolazione e le Politiche Sociali-Consiglio Nazionale delle Ricerche, Italy Leopoldina Fortunati, Università degli Studi di Udine, Italy Tiziana Guzzo, Istituto di Ricerche sulla Popolazione e le Politiche Sociali-Consiglio Nazionale delle Ricerche, Italy This chapter describes cause-effect connections between changes that mobile devices, such as mobile phones, PDAs, iPods and smart phones have on improving and modifying the learning process, and how this improvement is modifying the learning tools and the environment in which learning takes place. Section IV Standards, Guidelines, and Evaluation of Multimodal Systems This section presents the emerging standards and guidelines for mobile multimodal applications design and usability evaluation. Standards for multimodal interaction provided by W3C are described and a visual interaction design process for mobile multimodal systems is sketched. Finally, a selection of “classical” methods and some new developed methods for testing usability in the area of multimodal interfaces are given.

Chapter XXII Standards for Multimodal Interaction ................................................................................................. 409 Deborah A. Dahl, Conversational Technologies, USA This chapter presents current and emerging standards supporting multimodal applications with a particular focus on voice and GUI interaction. Among the major standards, are described the W3C multimodal architecture, VoiceXML, SCXML, EMMA, and speech grammar standard. Finally, the chapter highlights how standards can represent a basis for the future of multimodal applications. Chapter XXIII Visualising Interactions on Mobile Multimodal Systems ................................................................... 430 Kristine Deray, University of Technology, Sydney, Australia Simeon Simoff, University of Western Sydney, Australia This chapter, presenting a framework for designing visual representations for mobile multimodal systems, traces some guidelines. The chapter is focused on the output modalities. The proposed guidelines are grouped in “two sets: (i) a set of high-level design requirements for visual representations of interactions on mobile multimodal systems; and (ii) a set of ten specific design requirements for the visual elements and displays for representing interactions on mobile multimodal systems.” Chapter XXIV Usability Evaluation of Multimodal Interfaces .................................................................................. 443 Regina Bernhaupt, IRIT, IHCS, Toulouse, France This chapter, concluding the book, faces one of the most relevant aspects when designing, implementing, and using an interface: its usability. Usability is a focal point for the wide diffusion of each kind of systems. Multimodality, jointly with mobile devices, is an element that can contribute to reach this goal. However, a mobile device with a multimodal interface is not necessarily usable. For this reason this chapter presents a selection of “classical” methods, and introduces some new developed methods for testing usability in the area of multimodal interfaces. Compilation of References ............................................................................................................... 456 About the Contributors .................................................................................................................... 495 Index ................................................................................................................................................... 507

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Preface

This book provides a contribution on the theories, techniques, and methods on multimodality and mobile devices for pervasive services. It consists of 24 chapters that provide an in-depth investigation of new approaches, methods, and trends. Humans communicate using their five senses in a synergistic manner expressing key-concepts involving different modalities and/or two or more modalities simultaneously. Indeed, human-human communication involves, in a synergistic manner, several communication channels; it can use gesture, sketch drawing, handwriting, facial expressions, gaze, and speech or their combination. Multimodal interaction systems can combine visual, auditory, and other kinds of information in a flexible and powerful dialogue manner, enabling users to choose one or more interaction modalities. The use of multimodality combined with mobile devices allows a simple and intuitive communication approach everywhere and every time. The synergistic approach in using more than one modality to interact with computers—or more generally with other devices such as mobile devices–makes dialogue and communication flexible, natural, and robust also for its closeness with the human-human one. Effectiveness and naturalness of communication is particularly relevant for services. The great diffusion of mobile devices, along with the development of multimodal interaction, presents a new challenge for telecommunication companies and all organizations that can be involved in providing new services using mobile devices. One requirement for these services is that they and their information have to be accessible to every mobile situation. Even if in the last years several efforts have been carried out to provide computer and mobile devices interfaces with a similar flexibility, naturalness, and robustness, a lot of theoretical and technological problems need to be faced when designing and implementing very natural, flexible, and robust multimodal human computer interaction approaches. Naturalness, flexibility, and robustness of interaction approaches are key elements to produce new and pervasive services for people. In developing multimodal pervasive services, it is essential to consider perceptual speech, audio, and video quality for the optimum communication system design, the effective transmission planning, and management respect to the customer requirements.

BOOK OBJECTIVE The book aim is to provide theoretical and practical scenarios, concepts, methodologies, standards, definitions, and applications used to design and develop multimodal systems, focusing on mobile devices and pervasive services. It gives an overview of the existing works in this sector, discussing the different strategies adopted for the fusion input process, optimization processes on mobile devices, ambiguity and error handling related to one or more modalities, the fission process, the personalization and adaptation

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of multimodal mobile services, and the accessibility and usability criteria. Moreover, the book contains some significant examples of pervasive multimodal mobile applications; it discusses as acceptance is the basic condition for a wide use of each service, and it analyses transformations produced in some sectors, such as, for example, e-learning, using both multimodal interaction and mobile devices.

AUDIENCE Academics, researchers, technicians, students in computer science, and all experts involved in designing multimodal and mobile pervasive services will find this book a valuable contribution to their knowledge and they will find it a stimulus for new scientific activities on the important topics involved.

ORGANIZATION The book contains 24 chapters organized in four sections. The first section introduces some of the basic concepts and describes the main theoretical and practical problems and features of multimodal interaction. The second section describes how some particular multimodal applications overcome design problems connected with combining different modalities and how ontologies can be useful to improve interoperability of different environments and naturalness of multimodal interaction. Chapters contained in the third section consider methods and problems of multimodal interaction focusing on mobile devices and considering their adaptability and usability features. This section introduces a discussion on changes on the communication and services fruition produced by using mobile devices in the particular scenario of e-learning. Finally, the fourth section of this book contains chapters that describe standards, some guidelines, and evaluation methods for multimodal systems. The first section is opened by a study on people acceptance and adoption of mobile devices proposed by Judy van Biljon and Karen Renaud in the first chapter. Acceptance is the first step for the wide adoption and diffusion of a new technology. In particular, the chapter investigates the “mobile phone adoption by older users” proposing the novel senior technology acceptance and adoption model (STAM), which incorporates acceptance factors into the adoption process. From Chapter II to Chapter IX the basic concepts, theories, and technologies about multimodal interaction and multimodal systems are provided; these chapters give an overview of the lifecycle of information and data characterizing multimodal interaction. In particular, in Chapter II Ferri and Paolozzi give an overview of the main problems, definitions, and concepts related to multimodality, describing the “different approaches used in the multimodal interaction,” mainly addressing those approaches that use the new semantic technologies as well as the Semantic Web ones. Naturalness of multimodal interaction depends on its feature of involving all five human senses simultaneously. D’Ulizia, in Chapter III, provides an overview of the different approaches of the literature to combine and integrate different input modalities (fusion process). Based on state of art review on multimodal input fusion approaches, Chen and Sun in Chapter IV present a novel approach to multimodal input fusion based on speech and gesture, or speech and eye tracking, using a combinational categorical grammar. This approach has been validated for mobile context. “Naturalness and flexibility of the dialogue” can produce a very complex interpretation process and the presence of ambiguities can be a relevant problem to face. Caschera, in Chapter V analyses approaches that have to cope issues connected to the interpretation process. She provides a classification

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of ambiguities into recognition, segmentation, and target ambiguities, and describes methods for solving different classes of ambiguities. Naturalness of the multimodal interaction implies a symmetric communication between humans and machines (computers, mobile devices, and so on). For this reason when a user interacts with a multimodal system, s/he produces a multimodal input that is fused and interpreted. The multimodal system usually reacts and produces an output, which requires to be interpreted by the user. The output arrangement and organization (multimodal fission) is a focal point that is faced by Grifoni in Chapter VI. This chapter provides some basic concepts involved in the fission process design and describes some of the most relevant approaches discussed in the literature, with a particular attention on the mobile devices use. The problem of flexibility and capability to adapt functionalities on mobile devices to the users is faced in Chapter VII by Esposito, Basile, Di Mauro, and Ferilli. The chapter presents a two-phase construction of the user model: an initial static user model, and revised/adjusted model by considering the information collected by the user interaction with the device/context. Designing and developing human machine interfaces is a critical task due to the fact that it is a time consuming and expensive task. This fact is particularly evident for multimodal environments. Coninx, De Boeck, Raymaekers, and Vanacken with Chapter VIII propose a model-based user interface design process (MBUID), in which the interface is defined by means of high level of abstraction, which gives the opportunity for creating intermediate prototypes and user evaluation, ultimately resulting in better and cheaper virtual environment interfaces. This first section ends with Chapter IX addressing the concrete aspect of the temporal and spatial organization of human pointing actions for multimodal human computer interaction, faced by MüllerTomfelde and Chen. The role of pointing gestures combined with other modalities is presented and developers are provided “with substantial knowledge about the temporal-spatial organization of the pointing gesture.” The second section of this book collects specific methodologies for designing and implementing multimodal systems. In Chapter X, Castellina, Corno, and Pellegrino describe a multimodal system integrating speech-based and gaze-based inputs for interaction with a real desktop environment. The proposed approach shows how the combined use of auditive and visual clues permits mutual disambiguation in the interaction with a real desktop environment. Pittarello and Celentano, in their chapter titled “Multimodality and Environment Semantics,” provide a methodology devoted to identify the user-oriented features of the environment describing the physical environment at different levels of granularity, functionally, and geometrically. A scene-independent ontology permits a level of interoperability between different environments. The chapter ends presenting a case study about the creation of a guided tour through the indoors and outdoors of the town of Venice using a multimodal web browser. The importance in using an ontology for multimodal systems in different use cases has a further evidence in Chapter XII, “An Ontology Supporting an On-Board Vehicle Multimodal Interaction System,” by Pisanelli, Lazzari, Innocenti, and Zanetti. In that chapter, they introduce ontologies (Vehicle Security, Road and Traffic Security, Meteorological, Users’ Profiles and Travel) supporting an on-board vehicle multimodal interaction system, which enables semantic interoperability for cooperation between the road infrastructure and assisting the driver. Barricelli, Marcante, Mussio, Parasiliti, Provenza, Padula, and Scala, in Chapter XIII, propose a methodology supporting a collaborative and evolutionary design of an interactive system. The proposed approach uses the metaphor of the “workshop of an artisan.” It starts from empirical experience to handle problems faced on the field, considering the need to interact everywhere and every time with mobile devices.

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An important aspect that multimodality can face is connected with the possibility that it has to improve the information and services accessibility, and the possibility of any person to make use of them. Bacellar, Leal Ferriera, Silva da Silveira, Gurgel do Amaral, Leal Ferriera, and Rodrigues Nunes, in Chapter XIV titled, “Making the Web Accessible to the Visually Impaired,” face the problem of making accessible information to blind persons. A study aimed at identifying and defining usability guidance compliant with accessibility W3C directives to facilitate the interaction between visually impaired people, and their access to Web information and services, has been presented. The potentiality to improve the access of people having different physical, psychological, and cognitive attitudes to different information and services is one of the features of multimodal interaction that makes it very attractive for e-learning, and in particular for mobile learning. Spetch, in Chapter XV depicts the experience carried out in the European project RAFT. A widget based approach design for the user interface enables to build clients for a variety of hardware and devices in the learning environment, from mobile phones, PDAs, and tablet PCs. Section III discusses and details problems, features, and applications of multimodal interaction focusing the attention in the case of mobile devices. In chapter XVI, Frattini, Corvino, Gaudino, Petriccione, Scotto di Carlo, and Supino discuss how a speech driven interaction between user and service delivery systems may be the ideal solution for the development of ubiquitous and context aware applications; indeed, even if people have eyes and hands busy because they are making other activities too, they are usually able to interact using speech. The authors propose in their chapter an architecture for building mobile multimodal applications describing their experiences. “A GRID approach to providing multimodal context-sensitive mobile social service” is presented in Chapter XVII by Pierno, Scotto di Carlo, Magaldi, Russo, Supino, Romano, and Bevilacqua. When designing a mobile information system, it is not only important input-output channels management and information presentation, but also context-awareness. The authors have described a platform that supports the development of multichannel, multimodal, mobile context aware applications, and its use in an emergency management scenario. In Chapter XIII, Doyle, Bertolotto, and Wilson depict an application providing mapping services and discuss the importance of opportunely identifying the visualization and interaction modes. Multimodal interfaces for mobile GIS represent a challenge according to the need to use them in different context and by different users, simplifying the inherent complexity of the majority of GIS interfaces. The chapter describes the multimodal mobile GIS CoMPASS, which uses a combination of speech and gesture input. Using multimodal systems on mobile devices to access information and services can require to generate the user interface on-the-fly according to the different contents, devices, and user’s needs. Chapter XIX, by Encelle, Baptiste-Jessel, and Sèdes, presents an “Adapted Multimodal End-User Interfaces for XML-Content.” This system is characterized by a user interfaces for browsing particular XML content types that can be personalized; it is described in particular its use for visual impaired people. The usability of mobile devices with their small screen is faced by Davcev, Trajkovik, and Gligorovska in Chapter XX, which describes a “Multimodal Mobile Virtual Blackboard” system. The system faces the addressed problem of adapting the application features to the specific user’s preferences and to the current use of the system. Adaptability is obtained by using XML agents and fuzzy logic. The wide diffusion of cell phones, smart phones, personal digital assistants, and other mobile devices, and the possibility to design and to implement a very natural multimodal interaction is changing the approach to many individual and social activities. D’Andrea, Ferri, Fortunati, and Guzzo in their chapter,

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“Mobile Devices to Support Advanced Forms of E-Learning,” describe that these transformation are producing a deep change in e-learning tools and in the environment in which e-learning takes place. The fourth section of this book discusses standards for multimodal applications, some guidelines to follow, in particular for visual representation of interaction for mobile multimodal systems. Finally, usability evaluation methods for multimodal applications on mobile devices are discussed. Dahl, in Chapter XXII describes standards for multimodal interaction focusing the presentation on standards for voice and GUI interaction (W3C Multimodal Architecture, VoiceXML, SCXML, EMMA, and speech grammar standards). Focusing on “Visual Representation of Interaction for Mobile Multimodal Systems,” Deray and Simoff, in Chapter XXIII, present a framework for designing visual representations for mobile multimodal systems, suggesting moreover a set of design guidelines. The final chapter of the book describes the relevant problem of usability evaluation for multimodal interfaces mainly for mobile applications and services. In this chapter, Bernhaupt depicts a selection of “classical” methods for usability evaluation and introduces some new emerging methods for testing usability of multimodal interfaces with a particular focus on mobile devices. Patrizia Grifoni Istituto di Ricerche sulla Popolazione e le Politiche Sociali Consiglio Nazionale delle Ricerche, Italy

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Acknowledgment

The editor would like to acknowledge the help of all involved in the collation and review process of the book, without whose support the project could not have been satisfactorily completed. Most of the authors of chapters included in this book also served as referees for chapters written by other authors. Thanks go to all those who provided constructive and comprehensive reviews. Special thanks go to all the staff at IGI Global, and in particular to Rebecca Beistline and Christine Bufton, for their continuous and professional support. In closing, I wish to thank all of the authors for their insights and excellent contributions to this book. Patrizia Grifoni Istituto di Ricerche sulla Popolazione e le Politiche Sociali Consiglio Nazionale delle Ricerche, Italy

Section I

Multimodality: Basic Concepts

The first section introduces some of the basic concepts and describes the main theoretical and practical problems and features of multimodal interaction, with a particular focus on mobile devices. Starting from a study on people acceptance and adoption of technologies and models, with a particular focus on mobile devices in the first chapter, it has been highlighted how naturalness of multimodal interaction facilitates its acceptance and its wide use diffusion by a large part of people. From chapter two to chapter nine the basic concepts, theories and technologies involved in the study of multimodal interaction and multimodal systems are provided.

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Chapter I

A Qualitative Study of the Applicability of Technology Acceptance Models to Senior Mobile Phone Users Judy van Biljon University of South Africa, South Africa Karen Renaud University of Glasgow, UK

ABsTRACT We report on an investigation into mobile phone adoption by older users. Technology adoption is a process from ignorance to considering it a necessity. Moreover, acceptance is an essential precursor of adoption. Many models consider either acceptance or adoption, but in fact these are interrelated. Furthermore, most theoretical models are based on responses from students or economically active adults. This begs the question: Do existing models incorporate the phases and the factors that lead to mobile phone adoption and acceptance by older adults? We thus studied the needs, uses, and limitations of older users and then gathered information about experiences and opinions of these users. We then mapped the verified acceptance factors against adoption processes in a two-dimensional matrix. This led to the proposal for the novel senior technology acceptance and adoption model (STAM), which incorporates acceptance factors into the adoption process, thereby consolidating the research in both these areas.

INTRODUCTION This chapter addresses technology acceptance and use in the context of the senior mobile phone user. These users are an oft neglected group in

product development and marketing, yet in most developed societies they are the only growing age group (Mallenius et al., 2007). The uptake of new technologies has been studied from two perspectives: acceptance factors and adoption process.

Copyright © 2009, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited.

Applicability of Technology Acceptance Models to Senior Mobile Phone Users

Several models of technology acceptance have been proposed and tested (Lee, 2007; Venkatesh et al., 2003). In the field of Management Information Systems (MIS), a number of technology acceptance models have been proposed, which focus on factors influencing acceptance without considering the process (Renaud & Van Biljon, 2008). Sociological studies take a meta-view by considering the adoption process, including technology’s acceptance, rejection and use (Haddon, 2003). Lee (2007) suggests that different approaches be merged in order to benefit from all their strengths. In this chapter we will be merging acceptance and adoption approaches since this enhances our understanding of factors influencing progression through the different adoption phases. It is important to consider acceptance and adoption together since adoption will not occur without a person having accepted the technology – the person may own the technology, but without accepting it he or she is unlikely to adopt it. Nonadoption means that it is unlikely to be utilised effectively or upgraded. In effect, significant non-adoption by any sector of society will lead to fewer sales and therefore market research is especially interested in acceptance. In predicting adoption, knowledge of the end users is as important as the functionality of a device (Holzinger et al., 2008). Current acceptance models have been quantitatively verified, using students or economically active adults as participants (Lee, 2007). This begs the question: Do existing models incorporate the phases and the factors that lead to mobile phone adoption and use by older adults? A literature study of the needs, limitations and expectations of the older adult mobile phone user made it clear that they demand added value in the form of a more social, active, meaningful and independent life (Mallenius et al., 2007). In terms of simple economics, the value of the phone can be expressed as value = f(usefulness, ease of use). How each factor is rated depends on each person’s individual needs (perceived usefulness) and abilities (perceived

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ease of use) and it is difficult to come up with a definitive way of measuring these. However, knowing which factors mediate ease of use and usefulness provides an insight into perceived value. The obvious matter for investigation is whether current mobile phones generally deliver this value to elderly users. We address this by verifying, qualitatively, the factors mentioned in the quantitatively derived models, and assessing their influence in the adoption process. Firstly, we need to identify a set of factors relevant to mobile phone acceptance. We constructed five scenarios related to senior mobile phone usage and presented these scenarios to participants in semi-structured interviews. The participants’ responses were used to validate or deny the identified factors. Furthermore we interviewed older users, asking about use of their mobile phones with a specific focus on ease of use. Our findings are presented in an acceptance matrix which maps the validated factors against the domestication technology adoption progression. This helps us to understand the factors that play a part in leading users down the road towards wholehearted adoption of a particular technology. The next two sections present the theoretical groundwork by presenting an overview of technology acceptance and adoption research. The following section examines the context of the elderly mobile phone user. Next we outline the qualitative study and present our results. The final section proposes the adoption matrix and presents the Senior Technology Adoption and Acceptance Model (STAM) and contextualises the findings.

TEChNOlOGy ADOPTION PROCEssEs There are two primary technology adoption process models: : Rogers’ innovation diffusion model (Rogers, 2003) and the domestication approach (Silverstone & Haddon, 1996). Rogers’


Table 1. Four dimensions of domestication adoption process (Lee, 2007) :66 Dimension

Description

Examples of potential themes relevant in user experience research

Appropriation

Process of possession or ownership of the artifact.

Motivation to buy a product Route to acquire information about a product Experience when purchasing a product

Objectification

Process of determining roles product will play

Meaning of a technology What function will be used in users’ life? Where is it placed? How is it carried?

Incorporation

Process of interacting with a product

Difficulties in using a product (usability problems) Learning process (use of instructional manual)

Conversion

Process of converting technology to intended feature use or interaction

Unintended use of product features Unintended way of user interaction Wish lists for future products

innovation diffusion model focuses on marketing and sales processes proposing various levels of progression from not having used the technology to full adoption. Rogers (2003) proposes the following five stage process of product adoption: the knowledge phase where the person gets to know about the product; the persuasion phase where he or she becomes persuaded of a need for the product; the decision phase which leads to a purchase; the implementation phase where the item is used and the confirmation phase where the individual seeks to confirm that he or she made the right decision in purchasing the product. Silverstone and Haddon (1996) proposed the domestication approach to deal with a more global analysis of acceptances ex post facto. They proposed the domestication of technology as a concept used to describe and analyze the processes of acceptance, rejection and use as described in Table 1 (Silverstone and Haddon, 1996). Users are seen as social entities and the model aims to provide a framework for understanding how technology innovations change, and are changed, by their social contexts. Clearly there are similarities between the two models. We selected the domestication theory adoption process for our purposes in charting acceptance factors since it is focuses on the ex post facto adoption process more than the decision to buy or not.

TEChNOlOGy ACCEPTANCE MODEls Examples of technology acceptance models are the Technology Acceptance Model (TAM) (Davis, 1989) and the Unified Theory of Acceptance and Use of Technology Model (UTAUT) (Venkatesh et al., 2003) each of which focus on technology acceptance within organisations (Ling, 2001). Here we present details of the different technology acceptance models and finally we consider technology acceptance models tailored specially to acceptance of mobile phones. From these we extract a set of factors relevant to mobile phone acceptance by senior users.

Technology Acceptance: Factors The Technology Acceptance Model (TAM) proposes a number of factors that are essential in determining user attitude towards accepting a new technology, as shown in Figure 1. (Davis, 1989; Malhotra & Galletta,1999). TAM incorporates six distinct factors (Davis, 1989; Pedersen, 2005): •

External variables (EV), such as demographic variables, influence perceived usefulness (PU) and perceived ease of use (PEU).

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Figure 1. Technology Acceptance Model (Davis, 1989) Perceived Usefulness (PU)

External

Attitude toward Use (A)

Variables (EV) Perceived Ease of Use (PEU)

Behavioural Intention to Use (BI)

Actual System Use

•

•

•

•

•

Perceived usefulness (PU) is defined as ‘the extent to which a person believes that using the system will enhance his or her job performance’ (Venkatesh et al., 2003). Perceived ease of use (PEU) is ‘the extent to which a person believes that using the system will be free of effort’ (Venkatesh et al., 2003) . Attitudes towards use (A) is defined as ‘the user’s desirability of his or her using the system’(Malhotra & Galletta, 1999). Perceived usefulness (PU) and perceived ease of use (PEU) are the sole determinants of attitude towards the technology system. Behavioural intention (BI) is predicted by attitude towards use (A) combined with perceived usefulness (PU Actual use (AU) is predicted by behavioural intention (BI).

Venkatesh et al. (2003) extended TAM and developed the Unified Theory of Acceptance and Use of Technology (UTAUT), which attempts to explain user intentions to use an information system and subsequent usage behaviour. An important contribution of UTAUT is to distinguish between factors determining use behaviour namely the constructs of performance expectancy,

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effort expectancy, social influence and facilitating conditions and then factors mediating the impact of these constructs. The mediating factors are gender, age, experience, and voluntariness (i.e. the degree to which use of the innovation is perceived as being of free will). Both TAM and UTAUT can be applied to any technology type but there is some value in specialising the models for particular technologies. The following section discusses the application of models in the mobile technology area.

Specific Models for Mobile Technology Acceptance Kwon and Chidambaram (2000) propose a model for mobile phone acceptance and use which includes the following components: demographic factors, socio-economic factors, ease of use, apprehensiveness, extrinsic motivation (perceived usefulness), intrinsic motivation (enjoyment, fun) social pressure and extent of use. They found that perceived ease of use significantly affected users’ extrinsic and intrinsic motivation, while apprehensiveness about cellular technology had a negative effect on intrinsic motivation (Kwon & Chidambaram, 2000). The limitation of this model is that it does not include infrastructural


Figure 2. Diagrammatic representation of MOPTAM(Van Biljon, 2007)

factors, which are essential in considering mobile technology. The Mobile Phone Technology Adoption Model (MOPTAM) (Van Biljon, 2007), depicted in Figure 2, integrates TAM with the determining and mediating factors from UTAUT and then adapts the result to model the personal mobile phone use of university students. Based on exploratory research, Sarker and Wells (2003) propose a framework that relates exploration and experimentation to the assessment of experience that determines acceptance outcome. The mediating factors distinguished are: context, technology characteristics, modality of mobility, communication/task characteristics and individual characteristics. Research on technology adoption of the elderly was done by Holzinger et al.(2008) and also by (Lee, 2007) but none of these studies proposed a new technology adoption model. Table 2 summarises the most fundamental factors incorporated into the models listing the following factors: Social influence (SI), Perceived Ease of Use(PEU), Perceived Usefulness (PU), Facilitating Conditions (FC), Behavioural Intention (BI), Demographic Factors (DF), Socio-Economic Factors(SE) and Personal Factors(PF). Note that

perceived ease of use is the common factor across all the models.

CONTExT OF ThE sENIOR ADUlT The mobile phone needs of the elderly centre around critical services such as emergency and health support that enhance safety and those services that make everyday life and tasks easier (Lee, 2007; Mallenius et al., 2007). The elderly position mobile phone use in terms of value, which is mostly based on communication, safety aspects and the support of independence (Holzinger et al., 2008, Lee, 2007, Van Biljon, 2007). Regarding perceived usefulness (PU) it can thus be said that senior mobile users use mobile phones in the following areas: •

Safety and security: Kurniawan (2007) claims a security-related focus drives the mobile phone market for older users. She says that older people use phones to make them feel less vulnerable when alone at home and more confident going out alone. They used their mobile phones mostly in emergencies or to contact family members. 5


Table 2. Factors influencing mobile phone acceptance Models and theories

•

•

•

Factors

TAM

UTAUT

Kwon & Chidambaram

Sarker & Wells

MOPTAM

SI

No

Yes

Yes

Yes

Yes

PEU

Yes

Yes

Yes

Yes

Yes

PU

Yes

No

No

Yes

Yes

FC

No

Yes

No

Yes

Yes

BI

Yes

Yes

Yes

No

Yes

DF

External variables

No

Yes

Yes

Yes

SE

External variables

No

Yes

Yes

Yes

PF

No

No

No

Yes

Yes

Communication: The importance of communication and the social influence is noted (Lee, 2007; Mallenius et al., 2007) but described in more detail under the social context. Organisation: The mobile phone offers the potential for organising and synchronising activities in new and cost effective ways. This organisation can be of a personal nature, such as using the reminders to compensate for memory failure, or the phone book to store telephone numbers. Information: The number of seniors surfing the Internet is steadily increasing (Ziefle & Bay, 2004), evidence of their need for, and readiness to use, the Internet to get information they require. The use of a mobile phone to access the Internet is an obvious next step for all users, and the elderly are no exception.

In the mobile context, the user and the equipment can be mobile and the surroundings may therefore change constantly. This opens up fundamental differences in the context of use between the traditional computing environments and information appliances such as mobile phones (Iacucci, 2001). Four different aspects of the mobile phone context have been noted (Jones & Marsden, 2005; Kiljander, 2004) in past research:

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physical context, social context, mental context and the technological context as discussed in the following sections.

Physical Context The physical context denotes the physical constraints of the usage environment (Jones & Marsden, 2005; Kiljander, 2004). We need to consider both the physical limitations of the device as well as the limitations of the surrounding physical context. Screen size, memory, storage space, input and output facilities are more limited in mobile devices such as mobile phones (Brewster, 2002; Young, 2003), while sound output quality is often poor with restricted voice recognition on input (Dunlop & Brewster, 2002). The undeniable potential of the ‘gray’ market is hampered by the physical and cognitive limitations of aging. Elderly mobile phone users make use of fewer mobile phone features than younger users (Lee, 2007). Ziefle and Bay ( 2004) suggest that elderly mobile phone users do not have a mental model of the ubiquitous hierarchical menu system used by mobile phones. They struggle to find the features they want to use and therefore do not use them. This is confirmed by a study carried out by Osman et al. (2003) who interviewed 17 elderly users and asked them to name the most important features of a mobile phone. ’Easy menus’ was


mentioned most often, followed by large screen. The latter is unsurprising since many elderly users have impaired vision. Another factor mentioned is that they require large buttons, due to the inevitable decrease in manual dexterity experienced by many elderly users (Lee, 2007). It follows that the effects of aging, such as impaired hearing, vision and loss of manual dexterity impact negatively on the ease of use of mobile phones. However, it would be a mistake to classify users strictly according to age. Mallenius et al. (2007) argue for using functional capacity (consisting of the physical, psychological and social aspects), rather than age as a facilitating condition.

social Context Social context concerns the social interaction involved and enabled by using the mobile device (Jones & Marsden, 2005; Kiljander, 2004). Phillips and Sternthal (1977) found that with increasing age comes reduced involvement with other people, as confirmed by Abascal and Civit (2001). The reasons are argued by experts, but the net effect is unarguable: reduced access to information that is readily available to younger people. Elderly people make extensive use of the television to give them the information they no longer get from other people (Phillips & Sternthal, 1977). The social contact they do have is primarily with their extended family and this group appears to provide them with the advice and support they need. Friends and relatives, especially the opinion of children and grand-children impact the behaviour of the elderly mobile phone user (Lee, 2007; Mallenius et al., 2007), therefore social influence as proposed in most of the technology acceptance model in Table 2, is an important factor in the mobile phone acceptance of older people.

Mental Context The mental context relates to aspects of the user’s understanding of the mobile handset usage model

(Kiljander, 2004). Mobile phones are acquired by a widespread population of users who will probably not have any formal training in operating them and consider them as devices to be used rather than computers to be maintained (Dunlop & Brewster, 2002). Furthermore, device vendors consolidate multiple functions into a single device. The mobile user has to handle interleaving of multiple activities and multiple public faces, previously unknown when only a landline or a stationary computer was used (Preece et al., 2002). Cognitive demands are exacerbated due to physical constraints on size, bandwidth and processing power, which restricts the communication bandwidth and places extra demands on the user’s attention (Hyyppä et al., 2000). The mental strain described above is amplified for the elderly mobile phone user, most of whom did not grow up with computers (Holzinger et al., 2008). People perform more slowly and with less precision as they age, elderly users appear to have difficulty learning how to use a new mobile phone (Botwinick, 1967) and use fewer of the available features (Van Biljon, 2007). The ability to learn is not impaired but the rate of learning is reduced (Baldi, 1997; Salthouse, 1985). Burke and Mackay (1997) mention that the formation of new memory connections is impaired with age. Therefore it is beneficial to allow elderly people to regulate their own rate of information processing. They struggle to filter out irrelevant stimuli so it takes them longer to process the relevant information in order to learn to use the device (Phillips & Sternthal, 1977). This is because they have reduced visual processing speed (Jackson & Owsley, 2003) and working through mobile phone menus is likely to be more difficult for them, purely because of this.

Technological Context The technological context refers to the mobile infrastructure including the networks available, services provided, costing and features of the

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mobile device (Jones & Marsden, 2005). Findings indicate that the elderly consider spending very carefully and the price of a device or service is a differentiator for use (Mallenius et al., 2007; Van Biljon, 2007). The mobile context poses unique challenges and opportunities in terms of mobility, portability and personalisation (Yuan & Zhang, 2003), and yet there is an overlap between the factors influencing mobile phone adoption and technology adoption in general (Kwon & Chidambaram, 2000). Therefore we will now consider factors from models for technology adoption as the basis for proposing a model for mobile phone adoption.

•

•

ThE qUAlITATIVE sTUDy Thirty four elderly people participated in our study (10 male and 24 female). The participants per age distribution were: 60-70 years: 13, 70-80 years: 16 and 80-90 years: 5. Considering mobile phone use, 19 of the participants had contracts and 15 used pre-pay. They obtained the phones by buying them (16), having the phone bought by someone else (3) or getting the phone as a gift (15). The majority who bought the phones themselves were in the 60-70 age group, i.e. the younger participants in our group.

scenarios Five scenarios seniors typically encountered in their everyday lives were presented to the participants and they were requested to comment. The participants responded actively, providing the following information: •

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Scenario 1 (obtaining information about mobile phones): Relating to information gathering, the responses fell into three groups: 9 said that people would ask their children; two said that they should ask people of their own age(not their children); while

•

•

23 reckoned that people would go to mobile phone vendors for information. Scenario 2 (accepting a cast-off phone): Relating to the impact of decreased ability to learn versus other motivations, three main groups of responses arose: 11 answered yes, citing the economical ‘You can sell the old one’; the philosophical ‘You should take the challenge’; and the pragmatic: ‘The old phone may be getting out of date’ as reasons. Seventeen answered no, stating memory loss and difficulty in learning as reasons. A third group of 6 reasoned that it depended on the person and the circumstances. Scenario 3 (emergencies such as having a stroke): Relating to safety and ease of use, 21 participants said that a mobile phone could be useful in emergencies, 12 felt that the older person would be ‘too scared and confused’, or ‘unable to find spectacles’. The rest felt that theoretically it was a good idea, but not practical since older people find phones difficult to use, even more so when stressed. Scenario 4 (accessory in helping to remember): Relating to the need for organisation, 28 reckoned that people could set a reminder, of these five had reservations about whether older people would manage to do that, whilst one was unsure that a mobile phone would be of any help. Scenario 5 (safety aid in travelling): Relating to safety and usefulness the majority(27) agreed that it could be useful, they gave different reasons such as the traveller contacting (phone or SMS) the family or vice versa, while some believed it could be used by a third party in the event of an emergency.

Interviews on Use It is clear that ease of use cannot really be selfreported. It is far more enlightening to observe users making use of a product. We therefore


asked participants to name the three features they used most often, and to show us how their phone performed the features. We intended to count the button presses in the second part of this process in order to gauge effort expended and consequent ease of use. We had to discontinue this since it became obvious that the participants had difficulty finding their most-used features. Some participants asked the experimenter for assistance in finding the feature, others tried various routes down the menu structure before triumphantly locating the desired feature. We felt that the button press count was so inaccurate as to be useless and therefore discontinued counting. Since the type of mobile possessed by the participants was diverse the unavoidable conclusion is that most phones have serious ease of use issues whatever make they are, as least as far as our participants were concerned.

DIsCUssION Sarker and Wells (2003) suggested an exploration and experimentation module, and certainly we had the sense that many of our participants had experimented with their phones soon after coming into possession of them. Some communicated a sense of frustration, verbalised as follows: I just can’t use that predictive text, even though my daughter has tried to show me how.

I am sure there is a way to get the phone to remind you about things, but I can’t find it. Please could you help me find my phone numbers – I have tried but I can’t find them. If the participants did indeed engage in an exploration phase, the obvious outcome of that phase would be the usage of features they had discovered and could use. Table 3 lists the features our participants told us they used regularly (see question 8 in Appendix A) and therefore depicts the outcome of their experimentation with, and exploration of, the phone. We confirmed that a general intention to use the phone plus their sense that the phone could be useful, when they first got it, resulted in a period of experimentation. However, their current use appeared to include only a minimal subset of features – mostly related to communicating as they would use traditional phones. Table 4 relates the acceptance factors identified during the interviews to the domestication dimensions to form a two-dimensional adoption matrix. Note that no evidence of conversion phase activities was uncovered while social influence is prevalent in all of the first three stages. For this group, perceived usefulness (PU) and perceived ease of use (PEU) are often not considered in the appropriation phase since many of the elderly do not select their own phones. This gap may provide an explanation for the observed lack of acceptance. Usefulness and ease of use are considered in the

Table 3. Feature use frequency (N=32) Feature

Sum

Associated factor

Phone book

24

user context – memory limitations

SMS

14

user context – economic limitations

Phone using number

11

user context – need for social contact

Alarm

9

perceived usefulness

Check Missed calls

4

social influence

Camera

4

social influence

9


objectification phase and incorporation phases where some concerns about usefulness, and serious concerns about ease of use, were identified. It became clear that participants fell into two distinct groups: those who had mastered the beast and those who were bemused by it. The former were more adventurous in their use of the phone, using more than just the minimal communication facilities. The latter appeared to own the phone merely because it had been given to them. They clearly saw that the phone could be useful, especially in emergencies, but they did not enjoy ownership the way the former group did. It appeared that for the latter group full adoption had not occurred—they had not converted to the technology—and this is likely to be due to a less than wholehearted acceptance of the technology. Our findings suggested that all users had explored the mobile phone but some of them found a number of features too difficult to locate and reverted to using it merely as a mechanism for phoning people when not at home – using a fraction of the functionality of the phone. As demonstrated, the two-dimensional adoption matrix that relates the technology acceptance factors that have emerged from quantitative research to the adoption phases identified through quantitative research can be useful in identifying and even predicting gaps in the technology adoption process. The challenges that face each generation or user group are different, but the in-

fluence of acceptance factors on adoption phases, as presented by the adoption matrix, will remain relevant. In the next section we discuss a model for modelling the context of the elderly mobile phone user based on this adoption matrix.

PROPOsING sTAM Based on the integration of the three main activities in our research approach: a literature study on technology adoption models, an investigation into the context of the senior user and the findings from our interviews, we proposed the Senior Technology Acceptance & Adoption Model (STAM) (Renaud & Van Biljon, 2008) as depicted in Figure 3. STAM consists of the following components, defined as: •

•

User Context such as demographic variables, social influence and personal factors such as age and functional ability, for example. Social influence is the prevalent external variable and therefore depicted as a module within the user context. Social influence aligns with Rogers’ observability innovation attribute. Perceived usefulness is defined as ‘the extent to which a person believes that using the system will enhance his or her job performance’ (Venkatesh et al., 2003). This

Table 4. Adoption matrix: Relating domestication progression to factors Factors Dimension

SI

PU

PEU

BI

Appropriation (Scenarios: 1,2)

Yes

No

No

No

Objectification (Scenarios 2,3,4)

Yes

Yes

Yes

Yes

Incorporation Identified in observation

Yes

Yes

Yes

Yes

Conversion

No evidence in observation or interviews

10


• •

•

•

•

•

aligns with Rogers compatibility and relative advantage innovation attribute (Rogers, 2003). Intention to Use is influenced by perceived usefulness and also by user context. Facilitating conditions refer to the mobile phone infrastructure and cost. This includes variables such as system service, system quality, cost of the handset and cost of the service as determined by the business model of the service providers. Experimentation and Exploration, which is the module where the user first starts using the technology and forms first impressions of the ease of use. Note that the experience obtained here will feed back into confirmed usefulness. The importance of this module confirms findings by Arning and Ziefle (2007) that performance was the main predictor of ease of use. It also aligns with Rogers’ (2003) trialiability innovation attribute. Ease of learning & use results from the perceived ease of use ie. ‘the extent to which a person believes that using the system will be free of effort’ (Venkatesh et al., 2003), and the final conclusion about ease of use is directly influenced by the experimentation and exploration stage. This aligns with Rogers’ (2003) complexity innovation attribute. Finally, whereas other models do not incorporate the ease of learning aspect, the senior model needs to, since difficulty in learning to use a device is a determining factor for the elderly (Ziefle & Bay, 2004) as is the fear of failure (Arning & Ziefle, 2007). Confirmed usefulness is the usefulness of the person’s phone to him or her – composed of the features he or she is able to learn to use. Actual use is indirectly predicted by the outcome of the experimentation, which leads to ease of learning & use. Facilitat-

ing conditions and the consequent ease of learning & use predict actual use. Finally, acceptance or rejection is predicted by ease of learning and use and actual use, with the former more strongly influencing acceptance. STAM, like UTAUT and MOPTAM does not include attitude as a determining factor. Van Biljon (2007) found no significant correlation between attitude towards use and any of the other determinants. This is supported by our observation that most people between the ages of 10 and 70 use mobile phones and indeed all our participants intended to use their phones. Dissatisfaction with the ease of use of the phone did not deter people from intending to use the phone – the social influences were far too strong to be offset by a phone that was difficult to use. What was affected by ease of use was actual use of the phone, and eventual acceptance. Figure 3 depicts the Senior Technology Acceptance and Adoption Model (STAM) which models both acceptance factors and adoption phases from first ownership towards actual acceptance or rejection. STAM captures the context of the elderly mobile phone user in an improved way since it incorporates technology acceptance factors into the adoption phases. For elderly people the appropriation phase (see Table 1) is often skipped. They seldom make the decision to buy as their first phone is often given to them or bought for them (note that fewer than 50% of our participants bought their current phone themselves). Several researchers have tested the applicability of TAM to specific groups. For example, technology adoption by physicians (Chismar & Wiley-Patton, 2003). Aggelidis and Chatzoglou (2008) worked on technology acceptance in hospitals while Fife and Pereira studied the adoption levels of mobile data services between and within national markets (Fife & Pereira, 2005). However, the elderly user is different from other users in one very important way.

11


Most technology adoption models incorporate a phase which represents the stage where the person makes the decision to purchase the new innovation. Rogers (2003) categorises people in terms of how long, after the product is launched, it takes for them to purchase the product. Hence, mobile phones are a special case because many of our participants did not purchase their mobile phones. Some were given the phones by children, others by friends. For this group it seems that Rogers’(2003) first three stages are skipped and the elderly user is often catapulted directly into the 4th stage: implementation, or the second stage of the domestication process, objectification, without being able to purchase the particular product that best suits his/her needs. It appears that intention to use is influenced primarily by external factors such as filial affection and safety and security concerns. From our observations it is clear that ease of use does not mediate intention to use – but it does influence actual use of the mobile phone. Many of our participants had phones, and pronounced themselves mobile phone users, but did not actually use the phone because of usability issues. In the objectification phase (see Table 1) determining the role the technology will play

manifests in the behavioural intention which is influenced by social factors and perceived usefulness. The incorporation phase describes the interaction with the technology as represented by the experimentation and exploration module. It is well known that the elderly consider spending very carefully and the price of a device or service is a differentiator for use (Mallenius et al., 2007; Van Biljon, 2007). This is depicted in the facilitating conditions module. Facilitating conditions, perceived usefulness and ease of learning and use all influence actual use. Acceptance implies that the user has progressed through all the phases without being derailed by the various mediating and determining factors. Rejection would result from a poor experimentation experience and a resulting perception that the device is too difficult to learn or to use. Whereas most other models imply eventual acceptance by all users, our experience suggests otherwise, and our model reflects this. STAM is not the first attempt at modelling technology acceptance by the elderly adult user. Arning and Ziefle (2007) studied the influence of TAM factors on performance and found a significant correlation between performance and ease of use. That correlation was even stronger for the elderly age group. This study’s findings thus

Figure 3. Senior Technology Acceptance & Adoption Model (STAM)

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confirm theirs about the dominant influence of ease of use in full-adoption. Phang et al. (2006) presented a model for representing the factors that influence intention to use, where perceived ease of use as well as perceived usefulness was found to be highly significant in determining intention to use. However, they found that age was not significant in determining intention to use. This, too, is consistent with our findings that the elderly clearly intend to use a mobile phone. However, actual use is clearly hampered by poor ease of use. While confirming the importance of perceived usefulness and ease of use as fundamental factors determining technology acceptance for this age group, there are also significant differences between these models because they focus on different components of the adoption process. In contrast STAM depicts the transition from usage to acceptance and conversion (adoption) – a step that some users will never take, since their progress is inhibited by poor ease of use and consequent less than optimal confirmed usefulness. Elderly people have the added complication of often skipping the appropriation phase and this provides a plausible explanation for the fact that some elderly mobile phone users never progress to acceptance and conversion. Towards contextualising this research it should be noted that existing technology acceptance models only partially model the factors that influence the acceptance and adoption of the mobile phone by the elderly. Value = f(usefulness,ease of use), and each of these aspects plays a vital role in contributing towards the value of the phone. Moreover, ease of use clearly prevents our users from utilising available functionality. Thus elderly users are getting far less than the optimal value out of their mobile phones. Another problem with existing technology acceptance models is that they model mainly the appropriation phase without due consideration of the other phases in the adoption process. This study provides evidence that intention to use does not necessarily lead to actual use and acceptance. Therefore adoption models should

incorporate acceptance, especially when they aim to support an understanding of technology adoption by groups with special needs.

CONClUsION This study investigated mobile phone acceptance by the elderly adult user. We considered existing technology acceptance models and extracted a set of factors that could influence mobile phone acceptance. These factors were filtered by considering the context of the elderly and then validated by means of semi-structured interviews that included the presentation scenarios. The main contribution of this study is the technology adoption matrix which was used as the basis for proposing STAM, a model for the adoption process as driven by the factors that influence mobile phone acceptance in the context of the elderly mobile phone user. By relating acceptance factors to adoption phases STAM provides an explanation why many elderly adults never reach the final adoption phase and never fully accept the technology. The adoption matrix can also be useful in modelling technology acceptance of other demographic groups and other technologies.

ACKNOWlEDGMENT We acknowledge the National Research Foundation of South Africa for financially supporting this project.

REFERENCEs Abascal, J., & Civit, A. (2001). Universal access to mobile telephony as a way to enhance the autonomy of elderly people. WUAUC 2001. Alcacer so Sal. Portugal.

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Aggelidis, V.P., & Chatzoglou, P.D. (2008). Using a modified technology acceptance model in hospitals. International Journal of Medical Informatics,.doi:10.1016/j.ijmedinf.2008.06.006 Arning, K., & Ziefle, M. (2007). Understanding age differences in pda acceptance and performance. Computers in Human Behaviour, 23, 2904-2927. Baldi, R.A. (1997). Training older adults to use the computer. Issues related to the workplace, attitudes, and training. Educational Gerontology, 23, 453-465. Botwinick, J. (1967). Cognitive processes in maturity and old age. Springer. Brewster, S. (2002). Overcoming the lack of screen space on mobile computers. Personal and Ubiquitous Computing, 6(3), 188-205. Burke, D.M., & Mackay, D.G. (1997). Memory, language, and ageing. Philosophical Transactions of the Royal Society B: Biological Sciences, 352(1363), 1845-1856. Chismar, W.G., & Wiley-Patton, S. (2003). Does the extended technology acceptance model apply to physicians? In Proceedings of the 36th Annual Hawaii International Conference on System Sciences, doi 10.1109/HICSS.2003.1174354. Davis, F.D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340. Dunlop, M., & Brewster, S. (2002). The challenge of mobile devices for human computer interaction. Personal and Ubiquitous Computing, 6(4), 235–236. Fife, E., & Pereira, F. (2005, June 2-3). Global acceptance of technology (gat) and demand for mobile data services. Hong Kong Mobility Roundtable. Haddon, L. (2003). Domestication and mobile telephony. In Machines that become us: The social

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context of personal communication technology (pp. 43-56). New Brunswick, NJ: Transaction Publishers. Holzinger, A., Searle, G., Kleinberger, T., Seffah, A., & Javahery, H. (2008). Investigating usability metrics for the design and development of applications for the elderly. ICCHP 2008, LNCS, 5105, 98-105. Hyyppä, K., Tamminen, S., Hautala, I., & Repokari, L. (2000, October 23-25). The effect of mental model guiding user’s action in mobile phone answering situations. Paper presented at the Electronical Proceedings from the 1st Nordic Conference on Computer Human Interaction, Stockholm, Sweden. Iacucci, G. (2001). Bridging observation and design in concept development for mobile services. In Third international symposium on human computer interaction with mobile devices, ihmhci. Lille, France. Jackson, G.R., & Owsley, C. (2003). Visual dysfunction, neurodegenerative diseases, and aging. Neurology and Clinical Neurophysiology, 21(3), 709-728. Jones, M., & Marsden, G. (2005). Mobile interaction design. Hoboken, NJ: John Wiley and Sons. Kiljander, H. (2004). Evolution and usability of mobile phone interaction styles. Unpublished doctoral dissertation, Helsinki University of Technology, Helsinki. Kurniawan, A. (2007). Interactions. Mobile Phone Design for Older Persons, July & Aug, 24-25. Kwon, H.S., & Chidambaram, L. (2000). A test of the technology acceptance model: The case of cellular telephone adoption. In Proceedings of the 33rd Hawaii International Conference on System Sciences, Hawaii (Vol. 1). IEEE Computer Society.


Lee, Y.S. (2007). Older adults’ user experiences with mobile phone: Identification of user clusters and user requirements. VA: Virginia Polytechnic Institute and State University. Ling, R. (2001). The diffusion of mobile telephony among Norwegian teens: A report from after the revolution. Paper presented at ICUST, Paris. Telenor R&D. Malhotra, Y., & Galletta, D.F. (1999). Extending the technology acceptance model to account for social influence: Theoretical bases and empirical validation. In Proceedings of the 32nd Annual Hawaii International Conference on System Sciences (Vol. 1). IEEE. Mallenius, S., Rossi, M., & Tuunainen, V.K. (2007). Factors affecting the adoption and use of mobile devices and services by elderly people – results from a pilot study. 6th Annual Mobile Round Table. Los Angeles, CA. Osman, Z., Maguir, M., & Tarkiainen, M. (2003). Older users’ requirements for location based services and mobile phones. LNCS, 2795, 352-357. Pedersen, E. (2005). Adoption of mobile internet services: An exploratory study of mobile commerce early adopters. Journal of Organizational Computing and Electronic Commerce, 15(3), 203-222. Phang, C.W.J., Sutano, A., Kankanhalli, L., Yan, B.C.Y., & Teo, H.H. (2006). Senior citizens’ acceptance of information systems: A study in the context of e-government services. IEEE Transactions on Engineering Management. Phillips, L., & Sternthal, B. (1977). Age differences in information processing: A perspective on the aged consumer. Journal of Marketing Research, 14(2), 444-457. Preece, J., Sharp, H., & Rogers, Y. (2002). Interaction design--beyond human-computer interaction. New York: John Wiley & Sons, Inc.

Renaud, K., & Van Biljon, K. (2008). Predicting technology acceptance by the elderly: A qualitative study. In C. Cilliers, L. Barnard, & R. Botha (Eds.), Proceedings of SAICSIT 2008 (Vol. 1, 210219). Wildernis, South Africa: ACM Conference Proceedings. Rogers, E.M. (2003). Diffusion of innovations (5th ed.). New York: The Free Press. Salthouse, T.A. (1985). Speed of behavior and its implications for cognition. In J.E. Birren, & K.W. Schaie (Eds.), Handbook of the psychology of aging (2nd ed.). New York: Van Nostrand Reinhold. Sarker, S., & Wells, J.D. (2003). Understanding mobile handheld device use and adoption. Communications of the ACM, 46(12), 35-40. Silverstone, R., & Haddon, L. (1996). Design and the domestication of information and communication technologies: Technical change and everyday life. In Communication by design: The politics of information and communication technologies (pp. 44-74). Oxford, UK: Oxford University. Van Biljon, J.A. (2007). A model for representing the motivational and cultural factors that influence mobile phone usage variety. (Doctoral dissertation, University of South Africa, Pretoria). Retrieved from http://etd.unisa.ac.za/ETD-db/theses/available/etd-09062007-131207/unrestricted/ thesis.pdf Venkatesh, V., Morris, M.G., Davis, G.B., & Davis, F.D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478. Young, T. (2003). Software interface design for small devices. Retrieved on October 12, 2006, from http://www.cs.ubc.ca/~trevor/writings/ SmallScreenDesign.pdf Yuan, Y., & Zhang, J.J. (2003). Towards an appropriate business model for m-commerce. Mobile Communications, 1(1-2), 35-56.

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Ziefle, M., & Bay, S. (2004). Mental models of a cellular phone menu. Comparing older and younger novice users. In Lecture Notes in Computer Science: Mobile Human-Computer Interaction (MobileHCI 2004), (LNCS 3160/2004, pp. 25-37).

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ENDNOTE 1

We use the terms senior, elderly and older adult to refer to people over the age of 60.


APPENDIx A: qUEsTIONNAIRE 1. What is your mother-tongue (first language that you learned to speak)? ……………… 2. Are you? [a]

Male

[b]

Female

3. How old are you? [a]

60- 69

[b]

70- 79

[c]

80 or older

4. How would you describe your general level of computer experience? [a]

None - I have never used a computer

[b]

Low - I have used a computer belonging to someone else

[d]

Medium - I own a computer

[e]

High - I am comfortable using a computer

5. Is your phone? [a]

Contract

[b]

Pay as you Go

6. Did you? [a] Buy your phone

[b]

It was bought for me

[c]

It was passed on by someone else

7. Scenarios presented in questionnaire: 1) Jim lives alone. One of his children has emigrated. He is 75 years old and needs to keep in touch. He has decided to get a mobile phone so he can receive pictures and messages. Who should he get advice from before he goes to buy a phone? 2)

Leslie is a 75 year old with a mobile phone, which was given to him by his daughter, and he has been using it for 2 years. He now feels confident using it. She has now renewed her contract and wants to give him her old Cell Phone. Do you think he will take it?

3)

Pam has had a stroke. She is worried that it will happen again. Do you think she could use her mobile phone in some way to make her feel less vulnerable?

4)

Peter, aged 85, needs to take his medication every day at 12 noon and he keeps forgetting. Can his mobile phone help him?

5)

Tim likes to travel alone now that he has retired. His family is concerned about him. He says they shouldn’t worry because he has his mobile phone with him. Is he right? What should he do to allay their fears?

17


APPENDIx A: qUEsTIONNAIRE 8. Tick features that the participant uses and record keys pressed to do so: Alarm

Games

Calculator

Torch

Calendar

Phone with Phone Book (save numbers)

Camera

Phone typing in number

Check missed calls

Photo album/gallery

SMS

Picture messaging

SMS with predictive text

Personalised ringtones

E-mail

Profiles(change volume etc. )

Transfer Money

Set reminders on calendar

FM radio

Stopwatch

Other? Features you would like to use but don’t know how to: ...

18

19

Chapter II

Analyzing Multimodal Interaction Fernando Ferri Istituto di Ricerche sulla Popolazione e le Politiche Sociali Consiglio Nazionale delle Ricerche, Italy Stefano Paolozzi Istituto di Ricerche sulla Popolazione e le Politiche Sociali Consiglio Nazionale delle Ricerche, Italy

ABsTRACT Human-to-human conversation remains such a significant part of our working activities because of its naturalness. Multimodal interaction systems combine visual information with voice, gestures, and other modalities to provide flexible and powerful dialogue approaches. The use of integrated multiple input modes enables users to benefit from the natural approach used in human communication. In this paper, after introducing some definitions and concepts related to multimodal environment, we describe the different approaches used in the multimodal interaction fields showing both theoretical research and multimodal systems implementation. In particular, we will address those approaches that use the new semantic technologies as well as the ones related to the Semantic Web fields.

INTRODUCTION AND BACKGROUND There is a great potential for combining speech and gestures and other “modalities” to improve human-computer interaction because this kind of communication resembles more and more the natural communication humans use every day with each other.

Nowadays, there is an increasing demand for a human-centred system architecture with which humans can naturally interact so that they do no longer have to adapt to the computers, but vice versa. Therefore, it is important that the user can interact with the system in the same way as with other humans, via different modalities such as speech, sketch, gestures, etc. This kind of multimodal human-machine interaction facilitates the


Analyzing Multimodal Interaction

communication for the user of course, whereas it is quite challenging from the system’s point of view. For example, we have to cope with spontaneous speech and gestures, bad acoustical and visual conditions, different dialects and different light conditions in a room and even ungrammatical or elliptical utterances which still have to be understood correctly by the system. Therefore, we need a multimodal interaction where missing or wrongly recognized information could be resolved by adding information from other knowledge sources. The advantages of multimodal interaction are evident if we consider practical examples. A typical example for multimodal man machine interaction which involve speech and gestures is the ”Put That There” from Bolt (Bolt, 1980). Since that time, lots of research has been done in the area of speech recognition and dialogue management so that we are now in the position to integrate continuous speech and to have a more natural interaction. Although the technology was much worse in these times, the vision was very similar: to build an integrated multimodal architecture which fulfils the human needs. The two modalities can complement each other easily so that ambiguities can be resolved by sensor fusion. This complementarity has already been evaluated by different researchers and the results showed that users are able to work with a multimodal system in a more robust and stable way than with a unimodal one. The analysis of the input of each modality could therefore serve for mutual disambiguation. For example, gestures can easily complement to the pure speech input for anaphora resolution. Another reason for multimodal interaction is the fact that in some cases the verbal description of a specific concept is too long or too complicated compared to the corresponding gesture (or even a sketch) and in these cases humans tend to prefer deictic gestures (or simple sketches) than spoken words. On the other hand, considering

20

for example the interaction of speech and gesture modalities, there are some cases, where, for example deictic gestures are not used because the object in question is too small, it is too far away from the user, it belongs to a group of objects, etc.; here, also the principles of Gestalt theory have to be taken into account which determine whether somebody pointed to a single object or to a group of objects. Moreover, there it has been also empirically demonstrated that the user performance is better in multimodal systems than in unimodal ones, as explained in several works (Oviatt, 1999; Cohen et al., 1997; Oviatt et al., 2004). Of course, it is clear that the importance to have a multimodal system than a unimodal one strictly depends on the type of action being performed by the user. For instance as mentioned by Oviatt (Oviatt, 1999), gesture-based inputs are advantageous, whenever spatial tasks have to be done. Although there are no actual spatial tasks in our case, there are some situations where the verbal description is much more difficult than a gesture and in these cases, users may prefer gestures. As shown by several studies, speech seems to be the more important modality which is supported by gestures as in natural human-human communication (Corradini et al., 2002) This means that the spoken language guides the interpretation of the gesture; for example, the use of demonstrative pronouns indicates the possible appearance of a gesture. Therefore, several studies have been proved that speech and gestures modalities are coexpressive (Quek et al., 2002; McNeill & Duncan, 2000) which means that they present the same semantic concept, although different modalities are used. This observation can be extended also to other modalities that may interact with speech modality. The advantage of using multimodal systems instead of adopting unimodal ones relies not only in the fact that there are more efficient, but also the number of critical errors can be reduced. In other words, the multimodal systems are more flexible


and less error-prone and most of the users prefer them compared to unimodal ones. Furthermore, as we have been proved by several studies, the combination of both modalities strictly depends on the semantics (Oviatt, 1999). The importance of multimodal systems is also related to the analysis of the interaction between the human user and the system itself. Multimodal interaction can be seen as a multimodal dialogue between humans and computers. In this view a semantic empowered structure, is needed to manage the different aspects of the interaction. The new generation Web, the “Semantic Web” can be a solution for solving this problem. General purpose ontologies and domain ontologies make up the infrastructure of the Semantic Web, which allow for accurate data representations with relations, and data inferences. Therefore we can assert that the needed semantic infrastructure can be an ontological infrastructure. In this paper we describe the different approaches used in the multimodal interaction fields showing both theoretical research and multimodal systems implementation. In particular we will address those approaches that use the new semantic technologies, such as ontologies from the Semantic Web field, and describe how these approaches can aid the interpretation of the multimodal dialogue between human and machines.

hUMAN INPUT MODAlITIEs AND lEVEls OF INTERACTION A human-computer interaction can be described in several layers, taking the user from a specific goal, formulating a task and/or subtasks, carrying out these actions whereas receiving feedback on the physical level, and evaluating the result. An action is usually started in order to achieve some specific goal in the user’s intention, this objective which has to be prepared, and finally presented and articulated through physical actions and utterances. The presentation and feedback

by the computer passes through several stages as well, before it can be displayed to the user, possibly in various modalities including speech, haptic, etc., in order to be successfully perceived by the user. In literature, we can discern three levels of human-computer interaction that involves different modalities, they are namely: semantic, syntactic, and lexical (Dix et al., 1993). However for more specific cases more levels can be described, for example Nielsen in his virtual protocol model (Nielsen, 1986), specified a task and a goal level above the semantic level, and an alphabetical and physical level below the lexical level. The Layered Protocol is another example of a more complex model (Taylor, 1988) used to describe the “multimodal dialogue” using the speech modality, but also applied to general user interface issues. When more modalities are included in the interaction, models have to be refined in order to be effective. Applying the Layered Protocol in the interaction, defines the concept of E-Feedback which has to do with expectations of the system of the user, and the I-Feedback which communicates the lower level interpretations of the user’s actions by the system (Engel et al., 1994). The human-computer interaction can be based on addressing all possible and available modalities, including visual, sound and speech related ones. Through these modalities, it is possible to involve all the five senses (seeing, hearing, smelling, tasting and feeling) in the interaction. For example our sense of touch, the tactual sense, has three sources: the signals from the receptors in the skin (that regulate the cutaneous sensitivity) informing our tactile sense, the receptors in the muscles and joints that regulate our awareness of the location, orientation and movement of body parts, and the signal that occurs when a person is actively moving by sending signals from the brain to the muscles. On another side haptic perception involves all three channels, which is usually the case when a person manipulates an object or interacts with systems through a physical interface (Loomis & Leederman, 1986). 21


In order to better analyse the interaction between humans and systems, it is necessary to study the different senses involved by each modalities; the majority of these studies have been done in the field of psychology of human perception. However, the majority of this research is based on stimulus-response paradigms in pre-fixed laboratory conditions. In the context of HCI research, we also need to take into account the whole interaction cycle, and preferably study the distinct modalities in more complex situations. Indeed, in real life, perception and action are closely linked. Therefore the work of Gibson is useful in the study of human-computer interaction, because of his characterization of “active perception” and the role of the context that represents an important part of the whole interaction (Gibson, 1979). Gibson proposed to “consider the senses as perceptual systems”, subdivided them in five categories (one for each sense) of systems, namely: Basic Orientation, Auditory, Haptic, Taste/Smell, and Visual. He also emphasised the activity in each system, such as listening, looking and touching rather than hearing, seeing and feeling. Finally, interactions can be performed in several “modes” (that are different from modalities), for instance a text modality or a manipulation modality. Moreover, human actions can also be unconscious actions and in some cases also involuntary. The description of modes is primarily based on the human output modalities with which it influences its environment and communicates with other people (possibly through the use of convenient technologies). The modes that have been studied in literature are namely: symbolic (e.g., text, speech, etc.), iconic, non-verbal, (e.g. accompanying symbolic mode), involuntary (or unconscious) and even subconscious. It is important to observe that these modes are often influenced by the different contexts: for example when typing on a keyboard the movements of the fingers (gestures), may have a different meaning than, tapping on a PDA or inputting a particular device. 22

MUlTIMODAl sysTEM ARChITECTURE In this section we present a general multimodal input architecture that can be useful to understand some issues related to the analysis of interaction in human-computer multimodal systems. The architecture we describe, attempts to be modular and domain independent and is based on the works of the University of Cambridge and the MIT. Most notably, dialog management components are neglected to focus on completing the system and handling multimodal inputs; the incorporation of a dialogue manager and knowledge base into this framework is straightforward. A figure of sample architecture is presented. In Fig. 1 the overall design of a multimodal system is presented at logical level, showing the interaction between components. As we can see in the figure the multimodal system takes multimodal inputs that derive from the fusion of different modalities (e.g. speech, sketch, gesture, etc.). Each modality is characterized by a different grammar that describe the features of the particular modality and that must be implemented by the system developer. This framework presents three kinds of component: •

•

•

A modality recogniser (for each modality) that translates user input according to the proper grammar (e.g. for speech recognizer this represent a module that translates the user’s spoken words in chunk of input information) A multimodal input fuser that takes input from various recognisers (for each modality recognisers) and fuses their results into a complete semantic frame for the application. An application that accepts semantic frames and provides user feedback through multimodal output.


Figure 1. Multimodal System Architecture Modality Grammar Modality Grammar

Modality Grammar

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Input Modality

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This configuration defines communication between components and isolation of the multimodal input fuser from specific recogniser technology and the application itself. Thus, the best recogniser technologies from different sources can be used and/or implemented in the overall system. In the general case, recognisers can be added to the multimodal system by writing an interface layer that converts the recogniser’s output to a convenient input that conforms to those accepted by the Multimodal Input Fusion module. To apply it to the specific application, only grammars need to be written to specify the allowable modality commands and the correspondent multimodal commands that are accepted.

sEMANTIC-BAsED MUlTIMODAl INTERACTION PATTERNs A number of researches have improved multimodal interaction techniques so that they appear

Multimodal Grammar

Multimodal Input Fusion

Multimodal Input

Application

“natural”, in a human sense. Each of them has used different kinds of procedures in order to enhance the best performance of their applications. Multimodal interaction patterns refer to the possible relationships between the inputs representing a multimodal production, for example in the temporal, spatial or semantic domain. Temporal relationships, in particular, are crucial for correctly interpreting multimodal interaction, in both human-to-human and human-to-computer communication. Multimodal input fusion systems rely on such knowledge to validate or refuse possible fusion of inputs. Most of the past implemented systems have employed artificially or arbitrary defined values, even though some methods using syntactic or semantic combinations based on machine learning have been developed (Gupta & Anastasakos, 2004). The qualitative and quantitative aspects of the temporal relationships have been analyzed in several researches in order to provide a better design of multimodal input fusion modules, but also to progress the fundamental understanding of human communication. 23


Figure 2. Sequential and simultaneous interaction seque ntial

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An interesting analysis of the preferred integration patterns when using pen and speech was defined in the QUICKSET system (Oviatt et al., 2004); on the basis of these researches a number of experiments were performed involving speech and handwriting. These experiments helped distinguish two groups of multimodal integrators, namely: (i) sequential and (ii) simultaneous. The first group produced multimodal inputs sequentially, one modality at a time, in other words each time interval is characterized by only one modality. The second group considers overlapped modalities, at least partially, in the time dimension, in other words each time interval can be characterized by one or more modalities. An example of the two groups is showed in Fig. 1 (for the sake of simplicity we show only speech and sketch modalities interaction). It is also important to state that both overlapping and non-overlapping interactions can also be divided in other six types of cooperation between modalities, namely Complementarity, Concur-

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rency, Equivalence, Redundancy, Specialization and Transfer (Martin, 1997). The authors also show that machine learning techniques could be used to quickly classify the user’s temporal pattern as soon as they begin to interact (Oviatt et al., 1997; Huang et al., 2006). Moreover, beside of the simple temporal analysis, their study also determined the types of tasks more likely to induce multimodal interaction, as opposed to unimodal interaction. It was found that spatial location commands (e.g. modify, move) represented about the 86% of all multimodal inputs, against the percentage decreases to 11% for selection commands (e.g. zoom, label), and 3% for function commands (e.g. print, scroll). Finally, the study also shown that the order of semantic constituents in multimodal interactions from their corpus was different from the order in spoken English, but mainly due to the position of the locative constituents at the beginning in multimodal, and at the end in corresponding spoken inputs (Oviatt et al., 1997).


The study of multimodal interaction patterns has been less prominent for other modality combinations, especially when input recognition is a major concern. Typical examples are the systems involving the gesture modality. In “Put that There” project, a sensor based on electromagnetic field variations has been used. That sensor is employed to obtain the position and orientation of the hand from a fixed transmitter. The “Put That There” device is a small cube, and requires a chord, hence is fairly cumbersome and obtrusive while only limited information can be obtained (Bolt, 1980). Data gloves that were employed later, allowed a more complex gesture inputs due to finger position sensing but as a hindrance they can prevent correct movements. Vision-based hand tracking and shape recognition became a reality a few years later, involving a range of algorithms and devices (e.g., single or stereo cameras). However, despite of the large amount of studies and the improvements in the devices creation, vision-based recognition rates are not yet satisfactory. Due to this fact, the possibility to successfully combining such inputs with other modalities is currently seen as the most promising path to success. Multimodal interaction patterns studies were also made in gesture and speech systems. A relevant example is the Hauptmann’s framework (Hauptmann, 1989). In this framework a set of methodologies and techniques to formalise and quantify speech and gesture interaction, with a view to automation, were defined. This work is based on the study of user preferences and intuitive behaviours when using speech and hand gesture to move a 3D virtual object. As a result of this study a set of very detailed statistics were reported on the usage of words, lexicon, syntactic structure, hand and finger gestures. These statistics highlights the type of multimodal interaction preferred by users and when it occurs. However, the results appear as an unstructured set of percentages, which are difficult to implement in a generic way and lack to represent a useful way for underlining a multimodal interaction system.

Finally, a psychological approach has been used to provide a formal framework for the study of speech and gesture interaction. For example, an interesting study has underlined the existence of low level relationships between gesture, gaze and speech, suggesting cross-modal segmentation based on specific features of those modalities (Quek et al., 2000). More recently, a Hidden Markov Model-based implementation was used to improve disambiguation of speech with gestures (Kettebekov, 2004).

MUlTIMODAl INTERACTION IN ThE sEMANTIC WEB The development of a context-aware, multimodal interface to the Semantic Web (Fensel et al. 2003), in other words, ontologies and semantic Web services, is a very interesting task since it combines many state-of-the-art technologies such as ontology definition, ontology management, advanced dialog systems, interface descriptions such as EMMA (Baggia et al., 2007), OWL-S (Martin et al., 2004), WSDL (Christensen et al., 2001), RDF (Beckett, 2004), SSML (Burnett et al., 2004) and composition of Web services. A lot of interest has been shown in studying methodologies and techniques for augmenting Semantic Web applications with multimodal interfaces. In this scenario mobile devices may serve as an easy-to-use user interaction device which can be queried by natural language speech or handwriting, and which can understand human hand gestures (for example through a mobile device camera). The main task of these researches is to develop a multimodal dialog interface providing a “natural” interaction for human users in the Human-Computer interaction paradigm (Pantic et al., 2006). Many challenging tasks, such as interaction design for mobile devices with restricted computing power, have to be addressed; practically the user should be able to use the

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mobile device as a question-answering system, using (as an example) speech and gestures to ask for information stored in form of ontologies, or other information like weather forecast accessible through Web services, Semantic Web pages (Web pages enriched with semantic annotation or wrapped by semantic agents), or the Internet. In last years, several researches have been made to incorporate semantic information in some way or another with multimodal environments. We can identify two lines of researches in this context: the first one focuses on system integration while the second other focuses on interaction. One interesting approach was developed by Irawati et al. (Irawati et al., 2005), (Irawati et al., 2006). They proposed a semantic virtual environment in which information can be classified into domain dependent and domain independent information. This information is represented through the use of convenient ontologies. The authors present their framework (MultiModal Manipulation for 3D Interaction framework, M3I) in which interaction devices such as keyboard, mouse, joystick, tracker, can be combined with speech modality to give a command to the system. They also define an object ontology based, on the authors’ idea, on the common sense knowledge which defines relationships between virtual objects. Taking into account the available user context and the pre-defined object ontology, semantic integration component integrates the interpretation result from input manager, and then sends the result to the interaction manager. Therefore, this result will be used and mapped into a proper object manipulation module. In this way the system can understand the user intention and assist him achieving his objective avoiding nonsensical manipulations. The authors state that in their solution domain dependent information is specific to the application while their domain independent information remains static. The exact coupling of the semantic information with the multimodal manipulation framework is not extensively discussed, but we

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suppose it to be strictly coupled with the domain independent information. An interesting work that is based on the incorporation of semantic information in a multi-user system was proposed, always in the context of virtual systems (Otto, 2005). In this work the authors introduced a framework which combines the W3C Resource Description Framework (RDF), with his world model which has a similar structure as VRML or x3D. The above two solutions have as drawback that their semantic information is strictly connected to the system, so it is difficult to interface with other systems. A first attempt at semantic interaction is the work of Kallman in which the framework, designed for simplicity, was created for Smart Objects. (Kallmann & Thalmann, 1999). This framework is developed in such a way that the virtual object aids the user to accomplish a preprogrammed possible interaction. Such objects are called Smart Objects, in the sense that they know how the user can interact with them, giving clues to aid the interaction. These objects have pre-defines interaction behaviours and are selfaware of how and where (the context) the user could interact with them. The work of Abacy et al. integrates action semantics, expressed by the use of proper rules, such that these actions can be used for action planning purposes by intelligent agents trying to achieve a particular objective or task (Abacy et al., 2005). Another interesting but slightly different approach has been proposed by Conti et al. They created a semi-automatic tool through which it is possible to understand the speech input of the user along with other modality of input (Conti et al., 2006). During the coding process of the application, it is possible to add extra semantic tags to the code which are, during the whole compilation, processed such that they can be used by the framework for understanding the input user, mapping his/her intention. This approach is very useful for speech input but it is hard to use these semantic tags for other goals.


Another reference resolution approach, that is interesting to remark, is based on the incorporation of several parameters such as a spatial references, features, common ground, naming and which are very similar to semantic information represented in ontologies (Pfeiffer & Latoschik, 2004). Reference resolution approach was also been addressed in the VIENA system (Wachsmuth et al., 1995; Wachsmuth et al., 1997). In this system objects and places could be referenced both by writing and by word speech input accompanied by mouse clicks. The system, as stated by the authors, can distinguish three different frames of reference during the whole analysis process. Namely they are: i) egocentric, that is based on the user’s view, that can be considered static during the interaction process, ii) intrinsic, that is based on particular object features (e.g., a selected front area of a desk, etc.), iii) extrinsic, that is based on a communication partner’s view. Martinez proposed an extension of existing open file formats (such as VRML and X3D) (Martinez, 2004). In this extension the file definition is enriched with metadata describing each object that is characterized by unique identifiers. Using this approach, Martinez proposes to use them as semantic information that can be combined with fuzzy logic for reference resolution, the resolving of the referent, which can be effectively used during interaction. The main problem of fuzzy logic, in this context, is that the system has to contain correct fuzzy sets that can be seen as a limit. An important concept to be explored is the Semantic reflection (Latoschik & Frolich, 2007). The current approach refers to an extensible, module-based architectures that must be able to separate specific application content from the internals component of the framework. This is a challenging task due to the close data and control flow coupling but distinct data representations between the various modules. For this problem, semantic reflection can represent a possible solution. It is an extension to the pre-existent reflection paradigm from object-oriented programming

languages. Their approach is mainly oriented to permit independent model to be easily integrated (e.g. physics, graphics and haptics). This approach grants a bi-directional communication between the implemented models. Gutiérrez et al. have presented (Gutiérrez et al., 2005) a system along with a conveniently implemented tool which allows, as stated by the authors, for real-time configuration of multimodal virtual environments. Both devices and interaction itself are presented using an xml-based formalism. Each different modality is then joined using their tool to define which particular device modality is activated by which interaction modality. SmartWEB represents another system that studies the multimodal interaction through the use of Ontological Knowledge Bases (Engel & Sonntag, 2007; Sonntag et al., 2007). The system prototype is based on a Personal Digital Assistant (PDA). The PDA serves as an easy-to-use user interaction device which can be queried by natural language speech or handwriting, and which can understand particular signs (e.g. hand gestures) on the PDA touch-screen. It is also possible to analyze head movements that can be perceived by the PDA camera. This approach shows the strength of Semantic Web technology for information gathering during multimodal interaction, especially the integration of multiple dialog components, and show how knowledge retrieval from ontologies and Web services can be combined with advanced interaction features based on multimodal dialogue. The ontological infrastructure of SmartWEB, the SWIntO (SmartWEB Integrated Ontology), is based on an upper model ontology realized by merging well chosen concepts from two established foundational ontologies, namely DOLCE (Gangemi et al., 2002) and SUMO (Niles & Pease, 2001). These two ontologies are merged in a unique one: the SmartWEB foundational ontology SMARTSUMO (Cimiano et al., 2004). From the analysis of these studies an important observation can be done: a key element in understanding multimodal interaction in particular for

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those semantic-based approaches is the reference resolution. As briefly introduced in the first part of this section, reference resolution is a process that finds the “most proper referents to referring expressions”. The term referring expression is a phrase that is given by a specific user during his/ her inputs (that can be typical for speech input, but it is valid also for other modalities) to refer to a specific entity (or entities). A referent is an entity (e.g. a specific object) to which the user wants to refer to.

CONClUsION Multimodal interaction provides the user with multiple modalities of interfacing with a system beyond the traditional input/output devices (keyboard, mouse, etc.). The user typically provides input in one or more modalities, and receives output in one or more modalities. Inputs are obviously subject to input processing. For instance, speech input may be input to a speech recognition engine, including, for instance, semantic interpretation in order to extract meaningful information. Analyzing multimodal interaction it is important because the real interaction between humans and systems may be conflicting, in particular in that the interpretations of the input may not be consistent (e.g. the user says “yes” but clicks on “no”). Semantic information attached to the interaction session can be useful to help the interpretation of the user’s will. In this paper we address the most interesting Semantic Web approaches used to give an added value to multimodal interaction systems.

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Martin, J.C. (1997). Toward intelligent cooperation between modalities: The example of a system enabling multimodal interaction with a map. In Proceedings of the International Conference on Artificial Intelligence (IJCAI’97) Workshop on Intelligent Multimodal Systems, Nagoya, Japan. Martınez, J. I. (2004). An intelligent guide for virtual environments with fuzzy queries and flexible management of stories. Unpublished doctoral dissertation, Universidad de Murcia. McNeill, D., & Duncan, S. (2000). Growth points in thinking-for speaking. In D. McNeill (Ed.), Language and Gesture. Cambridge, MA: Cambridge University Press. Nielsen, J. (1986). A virtual protocol model for computer-human interaction. International Journal Man-Machine Studies, 24, 301-312. Niles, I., & Pease, A. (2001, October). Towards a standard upper ontology. In C. Welty, & B. Smith (Eds.), Proceedings of the 2nd International Conference on Formal Ontology in Information Systems (FOIS-2001), Ogunquit, ME. Otto, K. A. (2005). The semantics of multiuser virtual environments. In Workshop towards SVE (pp. 35-39). Oviatt, S. (1999). Mutual disambiguation of recognition errors in a multimodal architecture. In Proceedings of the Conference on Human Factors in Computing Systems (CHI’99) (pp. 576-583). New York: ACM Press. Oviatt, S. (1999, November). Ten myths of multimodal interaction. In Communications of the ACM, 42(11), 74-81. Oviatt, S., Coulston, R., & Lunsford, R. (2004, October 13-15). When do we interact multimodally? Cognitive load and multimodal communication pattern. In the 6th International Conference on Multimodal Interfaces (ICMI 2004), State College, PA (pp. 129-136). New York: ACM Press.

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Oviatt, S., Coulston, R., Tomko, S., Xiao, B., Lunsford, R., Wesson, M., & Carmichael, L. (2003, November 5-7). Toward a theory of organized multimodal integration patterns during humancomputer interaction. In the 5th International Conference on Multimodal Interfaces (ICMI 2003), Vancouver, Canada (pp. 44-51). New York: ACM Press. Oviatt, S., DeAngeli, A., & Kuhn, K. (1997, March 22-27). Integration and synchronization of input modes during multimodal human-computer interaction. In SIGCHI Conference on Human Factors in Computing Systems, Atlanta, GA (pp. 415-422). Pfeiffer, T., & Latoschik, E. (2004). Resolving object references in multimodal dialogues for immersive virtual environments. In Proceedings of the IEEE VR2004, Chicago, IL (pp. 35-42). Pantic, M., Pentland, A., Nijholt, A., & Huang, T. (2006). Human computing and machine understanding of human behavior: A survey. In Proceedings of the 8th International conference on Multimodal Interfaces (ICMI ’06), New York, NY (pp. 239-248). ACM Press. Quek, F., McNeill, D., Bryll, R., Kirbas, C., Arlsan, H., McCullough, K.E., Furuyama, N., & Gesture, A.R. (2000, June 13-15). Speech and gaze cues for discourse segmentation. In IEEE Conference on computer Vision and Pattern Recognition (CVPR 2000), Hilton Head Island, SC (pp. 247-254). Quek, F., McNeill, D., Bryll, B., Duncan, S., Ma, X., Kirbas, C., McCullough, K.-E., & Ansari, R. (2002, September). Multimodal human discourse: Gesture and speech. ACM Transactions on Computer-Human Interaction (TOCHI), 9(3). Sonntag, D., Engel, R., Herzog, G., Pfalzgraf, A., Pfleger, N., Romanelli, M., & Reithinger, N. (2007). SmartWeb handheld - multimodal interaction with ontological knowledge bases and Semantic Web services. Artifical Intelligence for Human Computing 2007, 272-295.


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KEy TERMs AND DEFINITIONs Gestalt Theory: Gestalt psychology (also Gestalt of the Berlin School) is a theory of mind and brain that proposes that the operational principle of the brain is holistic, parallel, and analog, with self-organizing tendencies; or, that the whole is different than the sum of its parts. The classic Gestalt example is a soap bubble, whose spherical shape is not defined by a rigid template, or a mathematical formula, but rather it emerges spontaneously by the parallel action of surface tension acting at all points in the surface simultaneously. This is in contrast to the “atomistic” principle of operation of the digital computer, where every computation is broken down into a sequence of simple steps, each of which is computed independently of the problem as a whole. The Gestalt effect refers to the form-forming capability of our senses, particularly with respect to the visual recognition of figures and whole forms instead of just a collection of simple lines and curves. Modality: The term is used to describe the distinct method of operation within a computer system, in which the same user input can produce different results depending of the state of the computer. It also defines the mode of communication according to human senses or type of computer input devices. In terms of human

senses the categories are sight, touch, hearing, smell, and taste. In terms of computer input devices we have modalities that are equivalent to human senses: cameras (sight), haptic sensors (touch), microphones (hearing), olfactory (smell), and even taste. In addition, however, there are input devices that do not map directly to human senses: keyboard, mouse, writing tablet, motion input (e.g., the device itself is moved for interaction), and many others. Multimodality: By definition, “multimodal” should refer to using more than one modality, regardless of the nature of the modalities. However, many researchers use the term “multimodal” referring specifically to modalities that are commonly used in communication between people, such as speech, gestures, handwriting, and gaze. Multimodality seamlessly combines graphics, text and audio output with speech, text, and touch input to deliver a dramatically enhanced end user experience. When compared to a single-mode of interface in which the user can only use either voice/ audio or visual modes, multimodal applications gives them multiple options for inputting and receiving information. Multimodal Fusion: A strong argument for multimodal input processing is what is generally referred to as multimodal fusion. By combining information coming from different modalities it is possible to improve recognition quality and/or confidence. However, multimodal fusion relies fundamentally on different modalities containing redundant information. It is important to distinguish between fusion at the signal level and fusion at the semantic level. In the case of lip movements and speech, fusion is theoretically possible at the signal level, while in the famous “put that there” example of deictic dereferencing, fusion is possible (and necessary) only at the semantic level. For semantic fusion to operate, both modalities need to have their own independent level of accuracy and confidence.

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Multimodal System: In the general sense, a multimodal system is defined as the particular system which supports communication with the user through different modalities such as voice, handwriting, gesture, emotions, etc. Literally, the term “multi” refers to “more than one” and the term “modal” may cover the notion of “modality” as well as that of “mode”. Modality refers to the type of communication channel used to acquire information. It also covers the way an idea is expressed or perceived, or the manner an action is performed. Mode refers to a state that determines the way information is interpreted to extract or convey meaning. In an interaction process, whether it is between humans or between human users and a computer system, both the modality and the mode come into play. The modality defines the type of data exchanged whereas the mode determines the context in which the data is interpreted. Therefore, if we consider a more system-centered view, multimodality can be seen as the capacity of the system to communicate with a user by means of different types of communication channels and to extract and convey meaning automatically. It is possible to state that both multimedia and multimodal systems use multiple communication channels. But in addition, a multimodal system is able to automatically model the content of the information at a higher level of abstraction NLP: The term is the acronym of Natural Language Processing (NLP). NLP is a range of computational techniques for analyzing and representing naturally occurring text (free text) at one or more levels of linguistic analysis (e.g., morphological, syntactic, semantic, pragmatic) for the purpose of achieving human-like language processing for knowledge-intensive applications. NLP includes: •

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Speech synthesis: Although this may not at first sight appear very ‘intelligent’, the synthesis of natural-sounding speech is technically complex and almost certainly

•

•

•

requires some understanding of what is being spoken to ensure, for example, correct intonation; Speech recognition: Basically the reduction of continuous sound waves to discrete words. Natural language understanding: Here treated as moving from isolated words to “meaning”; Natural language generation: generating appropriate natural language responses to unpredictable inputs. Machine translation: Translating one natural language into another.

Resource Description Framework (RDF): RDF is the standard for encoding metadata and other knowledge on the Semantic Web. In the Semantic Web, computer applications make use of structured information spread in a distributed and decentralized way throughout the current Web. RDF is an abstract model, a way to break down knowledge into discrete pieces, and while it is most popularly known for its RDF/XML syntax, RDF can be stored in a variety of formats. This article discusses the abstract RDF model, two concrete serialization formats, how RDF is used and how it differs from plain XML, higher-level RDF semantics, best practices for deployment, and querying RDF data sources. Semantic Web: The Semantic Web is an evolving extension of the World Wide Web in which the semantics of information and services on the Web is defined, making it possible for the Web to understand and satisfy the requests of people and machines to use the Web content. It derives from Tim Berners-Lee’s vision of the Web as a universal medium for data, information, and knowledge exchange. The Semantic Web core comprises a set of design principles, collaborative working groups, and a variety of enabling technologies. Some elements of the Semantic Web are expressed as prospective future possibilities that have yet to be implemented or realized.


Usability: The term identifies that quality of a system that makes it easy to learn, easy to use and encourages the user to regard the system as a positive help in getting the job done. Usability is defined by five quality components. Learnability that defines how easy it is for users to accomplish basic tasks the first time they encounter the design. Efficiency that defines users’ quickness in performing tasks. Memorability, that is important when users return to the design after a period of not using it, in order to define how easily they can re-establish proficiency. Errors, that defines how many errors users make, how severe are these errors, and how easily they can recover from errors. Satisfaction: that defines the satisfaction of the users using the systems. User: A person, organization, or other entity, that employs the services provided by an information processing system, for transfer of information. A user functions as a source or final destination of user information. An end user is any individual who runs an application program or system. User-Centered Design: The term identifies a design process in which the needs, wants, and limitations of the end user of an interface or an application play an important and extensive role. User-centered design can be characterized as a

multi-stage problem solving process that not only requires designers to analyze and foresee how users are likely to use an interface, but to test the validity of their assumptions with regards to user behaviour in real world tests with actual users. This kind of testing is necessary as it is often very difficult for the designers and the developers of an interface to understand intuitively what a first-time user of their design experiences, and what each user’s learning curve may look like. Virtual Environments Systems: Virtual environments systems are interactive, highly-intuitive computer systems that give users the illusion of displacement to another location. Generally the restrictive approaches define virtual environments as three-dimensional, real time, immersive, multi-sensorial and interactive simulations of a space that can be experienced by users via threedimensional input and output devices and through different I/O modalities (such as speech, sketch, gesture, handwriting, etc.). Virtual environments technology will better accommodate the unique characteristics of human users. Indeed, we can define virtual environments as interactive, virtual displays enhanced by special processing and by non-visual modalities (such as speech and haptic as an example), to convince users that they are immersed in a “virtual” space.

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Chapter III

Exploring Multimodal Input Fusion Strategies Arianna D’Ulizia Istituto di Ricerche sulla Popolazione e le Politiche Sociali Consiglio Nazionale delle Ricerche, Italy

ABsTRACT Human-computer interaction is a discipline that aims at enabling the interaction between humans and computational machines. In the last years, several efforts have been made to make this interaction more intuitive and natural. In this direction, multimodal interaction has emerged as the future paradigm of human-computer interaction. In order to enable a natural dialogue between users and computer systems, in multimodal systems the two main challenges to face are: to combine and integrate information from different input modalities (fusion process) and to generate appropriate output information (fission process). Our specific concern in this chapter is with the fusion of multiple input modalities.

INTRODUCTION In recent years, people are increasingly surrounded by objects in the everyday environment that are equipped with embedded software and wireless communication facilities and which they need to interact with. For instance, mobile phones, PDAs and portable PCs are used by an increasing amount of people to develop everyday activities. This phenomenon produces the need to simplify the

access to these technological devices by rendering human-computer interaction more similar to human-human communication. As a consequence, the “universal accessibility” concept is acquiring an important role in the research area of humancomputer interaction (HCI). This discipline deals with the design, development and implementation of computational systems devoted to enable the interaction with human beings. Three of the main emerging research directions of the HCI, in line


Exploring Multimodal Input Fusion Strategies

with the universal accessibility concept, are: (i) to make this interaction more intuitive, natural and efficient by integrating multiple input-output modalities, (ii) to enable a broader spectrum of users, with different ages and skill levels as well as users with disabilities, to access technological devices, and (iii) to increase the level of freedom offered to users. In particular, multimodal interaction, which refers to the simultaneous or alternative use of several modalities, has emerged as the future paradigm of human-computer interaction that advances the implementation of universal accessibility. This enhancement is also demonstrated by the studies of Oviatt et al. (Oviatt et al., 1997; Oviatt and Cohen, 2000; Oviatt et al., 2000) that emphasize the benefits of multimodal systems in terms of usability, accessibility, flexibility and efficiency compared to unimodal ones. In particular, a multimodal interaction improves usability, as it provides users with the means to choose among different available modalities. Secondly, multimodality improves accessibility to the device by encompassing a broader spectrum of users, enabling those of different ages and skill levels as well as users with disabilities. Finally, it offers improved flexibility and interaction efficiency. Several aspects characterize multimodal interaction compared to usual interaction through graphical user interfaces (GUIs). Firstly, a GUI requires atomic and unambiguous inputs (such as the selection of an element by mouse or the insertion of a character by keyboard), whereas a multimodal interaction involves several simultaneous inputs that have to be recognized and opportunely combined by managing the uncertainty of inputs through probabilistic techniques. The process of integrating information from various input modalities and combining them into a complete command is called multimodal fusion. Secondly, in a multimodal interaction temporal constraints of inputs have to be taken into account and consequently it requires a time-sensitive architecture and the recording of time intervals of each

modalities. Finally, in a GUI the output messages are conveyed to the user through a single medium (the graphical display), whereas in a multimodal system a way of disaggregating outputs through the various channels has to be found in order to provide the user with consistent feedback. This process is called multimodal fission, in contrast with multimodal fusion. Consequently, in the design and development of a multimodal system the two main challenges to face concern the multimodal fusion and fission processes. Our specific concern in this chapter is with the fusion of multiple input modalities. In particular, we intend to give a comprehensive analysis of current fusion approaches, considering two main classifications, namely according to the data fusion level (e.g. the fusion process takes places in the dialogue management system, as well as at grammar level) and the mathematical method (e.g. based on statistical or artificial intelligence techniques). The remainder of the chapter is organized as follows. After a brief introduction about basic terms and concepts of human-computer interaction theory, we investigate current multimodal fusion mechanisms, classifying them according to the data fusion level and the applied mathematical method. Final conclusions are given at the end of the chapter.

BACKGROUND In this section we review some basic terms and concepts of human-computer interaction theory. The success of the human-computer communication depends on the reaching of a common ground by exchanging information through the communication modalities. Such a communication modality refers to the medium or channel of communication that conveys information (Coutaz and Caelen, 1991). Multimodality refers to the quality of a system to allow more than one communication modality to be used during human-computer interaction. 35


A general model of multimodal human-computer communication is shown in Figure 1. Four different kinds of input/output communication modalities can be identified, according to the study of Schomaker et al. (Schomaker, 1995): •

•

The human output modalities, that are devoted to control and manipulate computational systems by achieving a high level of interactivity and naturalness of the multimodal interface. The speech is the dominant modality that carries most of the information content of a multimodal dialogue. However, gesture and gaze modalities are extensively studied in literature as efficient input modalities that are better suited to represent spatio-temporal information and are usually complementary modalities of the speech input; The human input channels, that are devoted to perceive and acquire information coming from the feedback channels of computational systems. The most frequently used perception channels are eyes, ears and touch, among which the first is the dominant input modality that receives the most information flow, followed by the auditive and tactile channels;

•

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The computer input channels, through which the computer gets information from the human output modalities. Some examples of computer input channels are microphone, camera, keyboard, mouse. Once acquired, the inputs need to be brought together and interpreted in order to give a coherent meaning to the multimodal act of the user; The computer output modalities, that are devoted to give feedback to the user, as, for instance, visual feedback, speech synthesizer, haptic feedback and so on.

In order to allow that the multimodal humancomputer communication process takes place successfully, the actions that the user expresses through the human output modalities have to be acquired by the system through the computer input modalities, and the human input channels of the user have to be able to perceive and understand feedback from the computer output channels. The informational flow that involves the human output and computer input modalities is named input flow, whereas the flow that involves the human input and computer output channels is named feedback flow. The multimodal fusion takes place during the input flow, therefore in this chapter we focus the discussion on this. In

Figure 1. The multimodal human-computer communication process

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particular, the acquisition and integration, by the side of the system, of the multimodal inputs that the user expresses through the human output modalities will be our specific topic. Concerning the human output modalities, six different types of cooperation between these modalities can be identified, as described in the typology proposed by Martin et al. (2001): •

•

•

•

•

•

Equivalence: Several modalities cooperate by equivalence if the same information may be processed as an alternative by either of them; Specialization: Modalities cooperate by specialization if a specific kind of information is always processed by the same modality; Redundancy: Modalities cooperate by redundancy if the same information is processed by these modalities; Complementarity: Several modalities cooperate by complementarity if different information are processed by each modality but have to be merged; Transfer: Modalities cooperate by transfer if information produced by a modality is used by another modality; Concurrency: Several modalities cooperate by concurrency if different information are processed by several modalities at the same time but must not be merged.

In multimodal systems, fusion techniques are mostly applied to complementary and redundant modalities in order to integrate the information provided by them. In particular, complementary modalities provide the system with non-redundant information that have to be merged in order to get a complete and meaningful message. In the same way, redundant modalities require a fusion process that avoids non-meaningful information, increasing, at the same time, the accuracy of the fused message by using one modality to disambiguate information in the other ones.

Having looked at conceptual aspects of multimodal communication, we now turn to architectural features of a multimodal system. A common architecture of a multimodal system (Oviatt, 2002), that involves speech, sketch and handwriting modalities, is depicted in Figure 2. During the acquisition phase, the input that the user expresses through these human output modalities is acquired through the appropriate computer input channels (touch-pad for sketch and handwriting, and microphone for speech) and processed by the related recognition modules (sketch and handwriting recognition and Natural Language Processing (NLP), respectively) in the subsequent recognition phase. Afterwards, the multimodal fusion system carries out the integration of the recognized inputs, by removing possible redundancy, merging complementary information from each modality and synchronizing the information in order to produce a meaningful and correct input. At this point, the dialogue management system aims at processing the integrated multimodal message/ command by activating appropriate applications and service in order to retrieve the output to be returned to the user (decision phase). The mapping between the input message expressed by the user and the corresponding output returned by the system is defined input interpretation. Thus the interpretation process involves, generally, four phases, corresponding to the main architectural levels of a multimodal system, from the top to the bottom (see Figure 2): the acquisition, recognition, integration and decision phases (levels). Although the acquisition, recognition and decision are consecutive phases, the same doesn’t occur for the integration phase (where the fusion process takes place), because in some systems the integration phase is prior to the recognition or decision phases, whereas in other systems it’s just the opposite. Multimodal systems have been largely studied since the 1980’s when the first original system “put-that-there” was developed by Bolt (1980). This system used speech and the location of a

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Figure 2. A common architecture of a multimodal system

cursor on a touchpad display to allow simple deictic reference, as for example “create a blue square here”. Note that a deictic is a word (e.g., “this”, “that”, “here”, “there”, etc.) that specifies identity or spatial or temporal location from the perspective of a speaker in the context in which the communication occurs. Deictic expressions are commonly used in multimodal interaction. As well as the “put-that-there” system, several attempts to overcome common graphical user interface have been made since the 1990’s until now (Neal and Shapiro, 1991; Nigay and Coutaz, 1995; Cohen et al. 1997; Vo, 1998; Wahlster et al. 2001). CUBRICON (Neal and Shapiro, 1991) used typed and spoken sentences and deictic mouse clicks as input in order to interact with a twodimensional map. MATIS (Multimodal Airline Travel Information System) (Nigay and Coutaz, 1995) allows the user to ask for information about the departure/arrival time of air flights by using

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speech and pen-based gesture modalities, along with mouse clicks and keyboarding. QuickSet (Cohen et al. 1997) was developed with the aim of training Californian military troops and used speech and pen-based gestures to interact with a geo-referenced map. QuickTour (Vo, 1998) is a multimodal system that enables a spoken and pen-based interaction to navigate geographical maps. The Smartkom (Wahlster et al. 2001) is another multimodal dialogue system that merges gesture, speech and facial expressions for both input and output via an anthropomorphic and affective user interface. In the next sections we illustrate some of the main characteristics of multimodal fusion, namely the data fusion level (e.g. the fusion process takes places in the dialogue management system, as well as at grammar level) and the adopted mathematical method (e.g. based on statistical or artificial intelligence techniques). We also provide several


examples of fusion approaches corresponding to the illustrated characteristics.

DATA FUsION lEVEls IN MUlTIMODAl FUsION The input signals, expressed by the user through the human output modalities and acquired by the system through the computer input modalities, can be combined at several different levels (Sharma et al. 1998). As introduced in the previous section, a multimodal system is composed of four main architectural levels (acquisition, recognition, integration and decision). The integration level, in which the fusion of the input signals is performed, may be placed: (i) immediately after the acquisition level and we refer to the fusion at the acquisition, or signal, level; (ii) immediately after the recognition level and in this case we refer to the fusion at the recognition, or feature, level; (iii) during the decision level and we refer to the fusion at the decision, or conceptual, level. The fusion at the acquisition level (see Figure 3.a) consists in mixing two or more, generally electrical, signals. As this kind of fusion may be performed if the signals are synchronized and of the same nature (two speech inputs, two sketch inputs, etc.) it cannot be applied to multimodal inputs, which may be of different nature. Consequently, we don’t take into account this level of fusion hereafter. The fusion at the recognition level (named also early fusion or recognition/feature-based fusion) consists in merging the outcomes of each recognizer by using integration mechanisms, such as, for example, statistical integration techniques, agent theory, hidden Markov models, artificial neural networks, etc. The integrated sentence is therefore processed by the decision manager that provides the most probable interpretation of the sentence (see Figure 3.b). Thus a unimodal recognition stage and an integrated decision stage characterize the interpretation process of the

early fusion. This strategy is generally preferred for closely and synchronized inputs that convey the same information (redundant modalities), as for example speech and lip movements for speech recognition or voice and video features for emotion recognition. The main drawbacks of the early fusion are the necessity of a large amount of data for the training, and the high computational costs. The fusion at the decision level (named also late fusion or decision/conceptual-based fusion) means merging neither the signals nor the features of each recognized input, but directly the semantic information that are extracted from the specific decision managers (see Figure 3.c). In fact, in this kind of fusion the outcomes of each recognizer are separately interpreted by the decision managers and the extracted semantic meanings are integrated by using specific dialogue-driven fusion procedures to yield the complete interpretation. Late fusion is mostly suitable for modalities that differ both in their nature and in the time scale. This implies that a tight synchrony among the various communicative modalities is essential to deliver the correct information at the right time. As each input modality is separately recognized and interpreted, the main advantages of this kind of fusion rely on the use of standard and well-tested recognizers and interpreters for each modality, as well as the greater simplicity of the fusion algorithms. In addition to these three levels of multimodal fusion, a fourth level, named hybrid multi-level fusion, can be identified (as described also in (Vo, 1998)). In this kind of fusion the integration of input signals is distributed among the acquisition, the recognition and decision levels. In particular, the interdependence among modalities, that allows predicting subsequent symbols knowing previous symbols in the input data flow, is exploited to improve accuracy of the interpretation process. This implies that a joint multimodal language model, which relies on the symbols acquired during the acquisition phase and is governed by

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their semantic meanings extracted during the decision phase, is the basis of the hybrid multilevel fusion strategy. To sum up, depending on the data fusion level at which the input signals are combined multimodal fusion strategies can be broadly classified as: recognition-based, decision-based and hybrid multi-level.

Recognition-Based Fusion strategies To achieve the integration of input signals at recognition level, multimodal systems have to rely on appropriate structures to represent these signals. In particular, three main representations

Figure 3. Possible levels of multimodal data fusion: a) fusion at signal level; b) fusion at recognition level; c) fusion at decision level

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can be found in literature, namely: action frame (Vo, 1998), input vectors (Pavlovic et al., 1997) and slots (Andre et al., 1998). In the approach based on action frame, proposed by Vo (1998), the multimodal input is regarded as a set of parallel information streams. Each stream represents one unimodal input coming from a computer input modality (e.g. a sequence of words and phrases in spoken modality, shapes in gestures, etc.) and consists of elements associated to a set of parameters. The integration of unimodal inputs consists in producing a sequence of input segments, named parameter slot, which separately contributes to the multimodal input interpretation, that is called action frame. Such an action frame specifies the action that has to be performed in response to the multimodal input. Each parameter slot specifies one action parameter and should contain enough information to determine the value of the corresponding parameter. The integration of the information streams is carried out through the training of a Multi-State Mutual Information Network (MS-MIN). More in detail, this network allows to find an input segmentation and a corresponding parameter slot assignment in order to extract the actual action parameters from the multimodal input. To achieve that the a posteriori probability of the parameter slot assignment conditional on the input segmentation is introduced. This probability is estimated by output activations in the MS-MIN network and can be interpreted as the score of a path that goes through the segmented parameter slots. An example of path over two multidimensional inputs (the spoken words “How far is it from here to there” and the drawing of an arrow between two points) is shown in Figure 4. Therefore, a path score maximization algorithm is applied to find the input segmentation and the corresponding parameter slot assignment. This algorithm creates an extra layer on top of the network. In particular, each output unit of the MS-MIN is an output state and the top layer of the network produces the best sequence of states

that fits the input, according to the path score maximization algorithm. The main advantage of this approach relies on the use of the MS-MIN network that allows the incremental and automatic learning of the mapping from input messages to output actions and the consequent improvement of the interpretation accuracy during the real use. The input vectors proposed by Pavlovic et al. (1997) are used to store the outputs of the visual and auditory interpretation modules. More in detail, the visual module firstly tracks the features of the video data by using skin color region segmentation and motion-based region tracking algorithms and the time series of the tracked features is stored into an input vector. Secondly, these features are dynamically classified by using Probabilistic Independence Networks (PINs) and Hidden Markov Models (HMMs). Therefore, the output of this module consists in a set of higher level features ranged from gestural movement elements, called visemes (e.g. “left movement”), to full gestural words (e.g. symbol for “rotate about x-axis). The auditory module has the same architecture and functioning of the visual module applied to audio data. A HMM PIN allows to classify the auditory features into auditory elements, called phones, and full spoken words. The integration of the two interaction modalities is carried out through a set of HMM PIN structures (see Figure 5), each corresponding to a predefined audio/visual command. The state of each HMM is defined according to the input vectors containing the high level features coming from the auditory and visual modules. As the multimodal integration occurs after a twostage recognition process (for audio and visual data, distinctly) and before the interpretation of the joint features has been performed, the fusion approach of Pavlovic et al., similarly to the action frame approach, can be classified as a recognitionbased fusion strategy. In the strategy based on slots (Andre et al., 1998), the information inputted by the user is stored into a slot buffer, which allows back referencing of past lexical units (e.g.: “it” can be used

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Figure 4. The output path of the MS-MIN of Vo (1998)

Figure 5. The multimodal integration approach of Pavlovic et al. (1997)

to reference the previously selected object). The command language of the application is encoded in semantic units called frames. The command frames are composed of slots, i.e. lexical units provided by the multimodal input. For instance, considering the “move frame” two slots can be identified: “object” (to specify the object) and “where” (to specify the final position). The frames are predefined (computed off line) and are application-dependent. The parser extracts the lexical units from different input modalities and

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fills the appropriate slots in the slot buffer. The slot buffer is continuously monitored checking for filled frames. Once a frame is filled (enough information to generate a command), the fusion agent sends it to be executed in the current application. The main advantage of this architecture is the uniform access of the input modes. In all the three fusion strategies, described above, the input signals are merged after recognizers have transformed them into a more appropriate representation (action frames, input


vectors, and slots) but before any interpretation has been assigned to the unimodal input. This has led us to classify them as recognition-based fusion strategies. The main advantage of these strategies relies on the great coherence with the human-human communication paradigm in which the dialogue is considered as a unique and multimodal communication act. Analogously, the recognition-based fusion strategies merge the recognized inputs into a unique multimodal sentence that has to be opportunely interpreted. Moreover, they allow an easier inter-modality disambiguation. The main drawbacks of the recognition-based fusion strategies consist in the significant computational load and the high dependency on time measures. This dependency implies as well a large amount of real data to train the network (both the MS-MIN and the PIN HMM).

Decision-Based Fusion Strategies In the decision-based approach, the outcomes of each recognizer are separately interpreted by specific decision managers and then sent to the dialogue management system that performs their integration by using specific dialogue-driven fusion procedures to yield the complete interpretation. To represent the partial interpretations coming from the decision managers and achieve the integration of input signals at decision level, past and actual multimodal systems employ several kinds of structures, namely: typed feature structures (Cohen et al., 1997; Johnston, 1998), melting pots (Nigay and Coutaz, 1995; Bouchet et al., 2004), semantic frames (Vo and Wood, 1996; Russ et al., 2005), and time-stamped lattices (Corradini et al., 2003). The typed feature structures, originally proposed by Carpenter (1992), are used by Cohen et al. (1997) to represent the semantic contributions of the different input modalities. In particular, this data structure consists of a two main elements: the type that specifies the class which the input

to be represented belongs to, and a collection of feature-value pairs, in which the values can be atoms or another feature structure. An example of typed feature structure representing the syntactic features of a proper noun is shown in Figure 6.a. Feature structures and atoms are assigned to hierarchically ordered types. The authors achieve the integration of spoken and gestural inputs through a unification operation over these typed feature structures. Such operation requires pairs of feature structures or pairs of atoms that are compatible in type and the result of the unification is the more specific feature structure or atom in the type hierarchy. Figure 6.c shows the unification of the two feature structures represented in Figures 6.a and 6.b., that is the syntactic features of the word “dog”. To select the best unified interpretation among the alternative solutions probabilities are associated with each unimodal input. This decision-based fusion strategy is implemented in QuickSet, a multimodal system briefly described in the background section. Johnston (1998) carries on the study of Cohen et al. (1997) introducing a grammar representation in which spoken phrases and pen gestures are the terminal elements of the grammar, referred to as lexical edges. Each lexical edge is assigned grammatical representations in the form of typed feature structures. For instance, to represent the spoken word ‘helicopter’ the feature structure in Figure 7 is created, where the cat feature indicates the basic category of the element, the content feature specifies the semantic content, and the remaining features represent the modality, temporal interval and the probability associated with the edge. Multimodal grammar rules are encoded as feature structure rule schemata that can be hierarchically ordered allowing the inheritance of basic constraints from general rule schemata. The application of these rules enables the unification of two candidate edges and the consequent fusion of the corresponding multimodal elements. Although these two approaches, based on typed feature structures, provide a generally applicable

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Figure 6. An example of typed feature structures unification

representation for the different modalities and the exploitation of well-known grammar-based techniques extensively explored in natural language processing, they show significant limitations on the expressivity and complexity. The fusion strategy based on melting pots, proposed by Nigay and Coutaz (1995), was originally implemented within the MATIS multimodal system, briefly introduced in the background section. As shown in Figure 8, a melting pot is a 2-D structure, in which the vertical axis contains the “structural parts”, i.e. the task objects generated by the input actions of the user, and the horizontal axis is the time. The fusion is performed within the dialogue manager by using a technique based on agents (PAC-Amodeus agents). Three criteria are used to trigger the fusion of melting pots. The first criterion, referred to as microtemporal fusion, is used to combine information that is produced either in parallel or over overlapping time intervals. The second criterion, called macrotemporal fusion, takes care of either sequential inputs or time intervals that do not overlap but belong to the same temporal window. A further criterion, referred to as contextual fusion, serves to combine input according to contextual constraints without attention to temporal constraints. 44

A refinement of the approach of Nigay and Coutaz (1995) has been carried out by Bouchet et al. (2004) and implemented in the ICARE (Interaction CARE - Complementarity Assignment, Redundancy and Equivalence) framework. Such framework considers both pure modalities, described through elementary components, and combined modalities, specified through composition components. Two kinds of elementary components are defined: the device components that abstract the captured input signals into recognized inputs, and the interaction language components that abstract the recognized inputs coming from the device components into commands. Finally, the composition components describe the fusion criteria of data provided by the elementary components, in line with the criteria defined in (Nigay and Coutaz, 1995). The main advantage of the ICARE approach relies on the component-based structure that allows to reduce production costs ensuring a high reusability and maintainability. In the approach based on semantic frames, proposed by Vo and Wood (1996), input from each modality is parsed and transformed into a semantic frame containing slots that specify command parameters. The information in these partial


Figure 7. An example of representation of a spoken word by typed feature structure

Figure 8. The structure of a melting pot (Nigay and Coutaz, 1995)

frames may be incomplete or ambiguous if not all elements of the command were expressed in a single modality. A domain independent framemerging algorithm combines the partial frames into a complete frame by selecting slot values from the partial frames to maximize a combined score. This approach is quite similar to the melting-pot strategy described above. The use of semantic frames with slots is followed also by Russ et al. (2005). As opposed to the previous fusion mechanism, in the approach of Russ et al. each slot (called main slot) contains

also the connections to a semantic network, as well as the attributes associated to each recognized input (contained into the attribute slots), as shown in Figure 9. A node in the network consists of a term and an activation value. If a connected node of the semantic network is activated, the slots of the frames are filled with the attributes as well as the activation values of the nodes. Therefore, the overall activation of a frame corresponds to the probability that the user input correlates with the frame. As each input can have multiple interpretations, this probability is taken into account

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to evaluate the best candidate interpretation. The main advantage of this approach is the uselessness of knowing a predetermined language or specific commands. In the approach based on time-stamped lattices, proposed by Corradini et al. (2003), each recognizer produces a set of interpretation hypotheses where each hypothesis stands for an independent and diverse interpretation of the input signal. These hypotheses are encoded by means of word lattices where several paths through the word lattice reflect the individual interpretations or n-best lists. The fusion engine combines the timestamped lattices received from the recognizers, selects its multimodal interpretation, and passes it on to the dialogue manager. The selection of the most probable interpretation is carried out by the dialogue manager that rules out inconsistent information by both binding the semantic attributes of different modalities and using environment content to disambiguate information from the single modalities. All the approaches introduced above occur at decision level, since individual input coming from the specific recognizers are partially interpreted before their integration. The main advantage of these strategies is the multi-tasking, as different multimodal channels, recognizers and interpreters are arranged for carrying out independent unimodal input processing at the same time. This implies also the possibility

to use standard and well-tested recognizers and interpreters for each modality. On the other hand, decision-based fusion strategies are characterized by a high complexity of the inter-modality disambiguation, particularly when dealing with more complex modalities that need not only pairs item-time but full lattices from each channel to disambiguate the multimodal input.

Hybrid Multi-Level Fusion Strategies In the hybrid multi-level approach, the integration of input signals is distributed among the acquisition, the recognition and decision levels. To parse multiple input streams and to combine their content into a single semantic representation three main methodologies have been applied in literature: finite-state transducers (Johnston and Bangalore, 2000), multimodal grammars (Sun et al., 2006; D’Ulizia et al., 2007) and dialogue moves (Perez et al., 2005). The approach based on finite-state transducers was proposed by Johnston et al. (2000). The authors perform multimodal parsing and understanding by using weighted finite-state transducers (FSTs) running on three tapes, in which the first tape represents the speech stream (words), the second the gesture stream (gesture symbols), and the third their combined meaning (meaning symbols). The transitions of the FST, which consist

Figure 9. The structure of the semantic frame of Russ et al. (2005)

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of an input and output symbol, are traversed if the input symbol matches the current recognized symbol and generates the corresponding output symbol. Figure 10 shows an example of transducer relating the spoken command “Email this person and that organization” and the gesture with the pen on the appropriate person and organization on the screen. Modality integration is carried out by merging and encoding into a FST both semantic and syntactic content from multiple streams. In this way, the structure and the interpretation of multimodal utterances by using FST is roughly equivalent to a context-free multimodal grammar that parses the inputs and yields the output tape providing semantic information. The FST approach is very versatile and provides a high degree of flexibility, allowing a huge spectrum of multimodal commands to be implemented. On the other hand, this approach does not support mutual disambiguation, i.e., using information from a recognized input to enable the processing of any other modality. Moreover, a huge amount of data is required to train the FST limiting portability. In the approach based on multimodal grammars, the outcomes of each recognizer are considered as terminal symbols of a formal grammar and consequently they are recognized by the parser as a unique multimodal sentence. Therefore, in the interpretation phase the parser uses the grammar specification (production rules) to interpret the sentence. This fusion strategy has been implemented in the MUMIF system (Sun et al., 2006). The fusion module of MUMIF applies a multimodal grammar to unify the recognized unimodal inputs into a unique multimodal input that is represented by using the TFS (Typed Feature Structures) structure proposed by Carpenter (1992). The MUMIF multimodal grammar is composed of two kinds of rules: lexical rules that are used to specify the TFS representation and grammar rules that constrain the unification of input.

The use of multimodal grammars is followed also by D’Ulizia et al. (2007). As opposed to the previous fusion mechanism, in the approach of D’Ulizia et al. a hybrid multimodal grammar is used, which encodes a Constraints Multiset Grammar (CMG, i.e. a kind of multi-dimensional grammar well-suited to specify the syntax of multimodal input) into Linear Logic in order to represent also semantic aspects. Once the set of terminal and non-terminal symbols, corresponding to the unimodal inputs coming from the recognizers, and the production rules are defined, the multimodal parsing process is carried out according to the production rules. The approach of D’Ulizia et al. allows the reasoning about multimodal languages providing a prerequisite for being able to resolve ambiguities and for integrating parsing with semantic theories. The main drawback relies on the high complexity. The dialogue moves are used by Perez et al. (2005) to represent multimodal user inputs coming from the lexical-syntactical analyzer. This structure, originally proposed by Quesada et al. (2000), is based on the DTAC, consisting of a feature-value structure with four main features, which are DMOVE, TYPE, ARG and CONT. An example of DTAC for the command “Turn on the kitchen light” is shown in Figure 11. The DTAC is quite similar to the typed feature structure of Carpenter (1992). This approach is implemented in the Delfos system, consisting of Multimodal input pool, a Natural Language Understanding (NLU) module and a collaborative dialogue manager. The multimodal input pool receives and stores all inputs (each one considered as an independent dialogue move) including information such as time and modality. The NLU module parses the input and adds further features in the DTAC structure, such as the modality of the event, the time at which the event started and ended. The dialogue manager checks the input pool regularly to retrieve the corresponding input. It operates by means of update unification rules, that define under what conditions the integration of DTAC

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Figure 10. A finite-state transducer in the approach of Johnston et al. (2000)

will happen. If more than one input is received during a certain time frame, further analysis is performed in order to determine whether those independent multimodal inputs are truly related or not. The main advantage of the hybrid multi-level fusion strategies relies on the similarity with the paradigm used in the human-human communication, in which the dialogue is considered as a unique linguistic phenomenon. On the other hand, they are characterized by a high complexity of the inter-modality disambiguation.

MAThEMATICAl METhODs IN MUlTIMODAl FUsION In this chapter we will investigate existing fusion approaches based on the mathematical method applied to perform the integration of the input signals. In the decision stage of the fusion process, statistical methodologies are often applied in order to take a decision on the interpretation of the multimodal sentence according to the knowledge of the acquired input signals. Classical statistical models applied in the literature are bayesian network, hidden markov models, and fuzzy logic. An alternative to statistical methodologies is represented by artificial intelligence-based techniques, such as neural networks and agent theory. These techniques, similarly to statistical ones, are well-suited for classification and recognition tasks in the multimodal fusion domain.

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Therefore, depending on the mathematical method applied to perform the integration of the input signals, multimodal fusion strategies can be broadly classified as: statistical and artificial intelligence-based.

statistical Fusion strategies The nature of the input signals, expressed by the user through the human output modalities and acquired by the system through the computer input modalities, can be characterized by a certain degree of uncertainty associated with the imperfection of data, frequently hard to recognize. To deal with this uncertainty statistical models, which consider previously observed data with respect to current data to derive the probability of an input, have been extensively used in the literature. In fact, most of multimodal systems, especially those that perform the fusion at recognition level, rely on statistical fusion strategies that use models of probability theory to combine information coming from different unimodal inputs. In particular, three main statistical methods can be applied in the fusion process: bayesian network (Toyama and Horvitz, 2000; Chu et al., 2004, Garg et al., 2003), hidden markov models (Zhihong et al., 2006; Gaitanis et al, 2007), and fuzzy logic (Lau et al., 2004). A large number of fusion techniques are based on bayesian network theory. In particular, Bayes’ theory is well suited to solve the decision problem of classifying an input into one class among several closed-set possibilities. This decision usually relies


Figure 11. An example of dialogue move in the approach of Perez et al. (2005)

on a maximum a-posteriori probability, assuming to know the a-priori and conditional probabilities of the input data set. A general formula for the evaluation of the a-posteriori probability is given in equation (1), where the a-posteriori probability Pr(Y|X) represents the belief of hypothesis Y given the information X . This probability is obtained by multiplying Pr(Y), the prior probability of the hypothesis Y, by Pr(X|Y), the probability of receiving X, given that Y is true. The estimation of this last function occurs through an approximation process, usually referred to as the likelihood function. The accuracy of this function depends on the quality of the model of the class. Pr(Y | X ) =

Pr( X | Y ) Pr(Y ) , Pr( X )

(1)

Toyama et al. (2000) employ a bayesian modality fusion to integrate the outputs of multiple visual tracking algorithms for a head tracking system. The Bayesian network model is instantiated with a set of observations including the modality report and the status of a set of context-sensitive indicators. An example of bayesian network model for fusing two modalities over time is depicted in Figure 12. The parameters of the network, that are the conditional probabilities of input events,

are learned through an offline training procedure that is able to convert the training data into a sets of probabilities representing the respective conditional contexts. The training of the network influence the fusion process, particularly the sparsity of the training data set causes inaccuracy and difficulties in the use of the model. Chu et al. (2004) use a hybrid multi-level fusion technique relied on a modified Bayesian classifier by adjusting the importance of the individual classifiers using exponential weights. Fusion is performed both at the feature and the decision level, thus enabling separability of the classes using minimum number of features. However, the product-combination technique suggested in their method is susceptible to noise. In addition to head tracking and speech-audio recognition systems, Bayesian networks have also been applied to the multimodal speaker detection task. In particular, Garg et al. (2003) developed a speech-based command and control interface for a Smart Kiosk. The input signals, coming from the camera and microphone of the kiosk, are captured by a set of five “off-the-shelf” visual and audio sensors: a neural network-based face detector, a gaussian skin color detector, a face texture detector, a mouth motion detector, and an audio

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Figure 12. An example of bayesian network model for fusing two modalities in the approach of Tomaya et al. (2000)

silence detector. The integration of these sensors is performed through a comprehensive bayesian network, which is composed of: •

•

•

50

A visual network, which fuses the outputs of the skin color, texture and face detector sensors in order to estimate whether a user is visible in front of the kiosk and if his/her mouth is moving; An audio network, which combines the outputs of the mouth motion and silence detector sensors in order to infer whether speech production is occurring; An integrated audiovisual network that combines the visual and audio sub-networks to the contextual information, as shown in Figure 13.

Bayesian network models have the attractive feature of combining an intuitive graphical representation with efficient algorithms for inference. However, the need of learning algorithms to model the standard parameters of the network can lead to non-optimal decisions in classification tasks. Hidden Markov Models (HMMs), either in their standard form or in their hierarchical variants, are another probabilistic graphical model, well suited for fusing different unimodal inputs. These models are a particular kind of dynamic bayesian network, which encodes dependencies among sets of random variables evolving in time. Gaitanis et al. (2007) apply these models to perform modality fusion at decision level. In particular, they defined a probabilistic multi-agent hidden markov model (M-AHMEM) in which each modality is modeled as an agent and the


Figure 13. The integrated audiovisual network of Garg et al. (2003)

silence detector. The integration of these sensors is performed through a comprehensive bayesian network, which is composed of: •

•

•

A visual network, which fuses the outputs of the skin color, texture and face detector sensors in order to estimate whether a user is visible in front of the kiosk and if his/her mouth is moving; An audio network, which combines the outputs of the mouth motion and silence detector sensors in order to infer whether speech production is occurring; An integrated audiovisual network that combines the visual and audio sub-networks to the contextual information, as shown in Figure 13.

Bayesian network models have the attractive feature of combining an intuitive graphical representation with efficient algorithms for inference. However, the need of learning algorithms to model the standard parameters of the network can lead to non-optimal decisions in classification tasks. Hidden Markov Models (HMMs), either in their standard form or in their hierarchical variants, are another probabilistic graphical model, well suited for fusing different unimodal inputs.

These models are a particular kind of dynamic bayesian network, which encodes dependencies among sets of random variables evolving in time. Gaitanis et al. (2007) apply these models to perform modality fusion at decision level. In particular, they defined a probabilistic multi-agent hidden markov model (M-AHMEM) in which each modality is modeled as an agent and the modality fusion is regarded as a cooperation that takes part inside the team formed by these agents. The M-AHMEM model represents the behaviours (policies) at different levels of abstraction and for different actors inside a team. Each policy, like a contingent plan, specifies the order of actions at all applicable states to achieve the goal. An example of representation of the action “Copy_object_O” through the M-AHMEM model is shown in Figure 14, where two agents are modeled, for gesture and voice, and a team that combines data from the two agents is defined. HMMs are also used by Pavlovic et al. (1997) to dynamically classify the visual and auditory features of video-audio data. A description of the functioning of their HMM-based approach has been given in section “Recognition-based fusion strategies” (see Figure 5). Fuzzy logic is another statistical method, used in the decision phase of the fusion process, that

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Figure 14. The representation of the action “Copy_object_O” through the M-AHMEM model of Gaitanis et al. (2007)

allows to incorporate effects of external conditions affecting the performance of the decision task. This statistical methodology has been applied by Lau et al. (2004) in a multimodal biometric system in order to verify the user’s identity through his/her facial features, voice and fingerprints. In order to process imprecise information due to adverse lighting conditions for face identification, mismatches in conditions under which the fingerprint identification is performed, or noise for the speech identification, six input fuzzy variables are used, each one having a fuzzy set that defines the favored external conditions of the variable. These conditions are formulated by three groups of fuzzy IF-THEN rules. Each group controls the output variable for the face, speech and fingerprint, respectively. An example of fuzzy rule for the face identification is:

Fuzzy logic generalizes probability and therefore is able to deal with approximate reasoning to draw (imprecise) conclusions from imprecise premises. Statistical fusion strategies have the main advantage that well-known algorithms exist for the estimation of the model parameters (EM algorithms, Maximum likelihood criteria, Maximuma-posteriori techniques,…) and for the search of the best model (Viterbi decoding, beam search,…). On the other hand, statistical fusion strategies are characterized by a lot of adjustments due to problems occurring in real contexts of use, such as the insufficiency of the training data, inaccuracy of the model, etc.

IF (FaceFindingConf is high) and (Illuminance is medium) THEN (wface is high).

Similarly to statistical methods, artificial intelligence techniques have been extensively applied by

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Artificial Intelligence-Based Fusion strategies


lots of multimodal systems to perform the fusion of input signals at recognition and decision levels. In particular, the most of the artificial intelligencebased fusion strategies use the agent theory Nigay and Coutaz, 1995) and neural networks (Meier et al., 2000; Lewis and Powers, 2002). The agent theory is applied by Nigay et al. (1995) in the MATIS system. As introduced in section “Decision-based fusion strategies”, this system adopts the PAC-Amodeus agents to perform the fusion at decision level. These cooperative agents are modelled as a three facet structures, where the control facet is the most important for the fusion as it manages the relationships with other agents. Every PAC agent has access to a fusion engine through its control facet. When calling the engine, a PAC agent provides a melting pot as an input and receives a list of melting pots as output parameter. The fusion of information is performed by exchanging melting pots across the hierarchy of agents. As one hierarchy per task exists, each hierarchy handles the melting pots that are related to the task it models. The root agent of the hierarchy maintains information about melting pots and the PAC agents interested by these melting pots. The use of the agent theory in the fusion process enables an automatic adaptivity to the input quality, taking into account eventual changes of the accuracy of inputs. Neural networks are used by Meier et al. (2000) in a speech recognition system that integrate auditory and visual (lip movements) signals. They adopted a particular kind of neural network, named Multiple State-Time Delayed Neural Network (MS-TDNN), which is composed of an acoustic and a visual TDNN. These two sub-nets are trained separately and their outputs are combined by the phonetic layer, as shown in Figure 15. The combined activation of this layer for a given phoneme is expressed as a weighted summation of the acoustic network activation and the corresponding visual unit.

Another approach based on neural networks and devoted to the integration of auditory and visual inputs for speech recognition has been proposed by Lewis et al. (2002). In particular, they adopted three neural networks, namely visual, acoustic and late integration networks. The fusion of auditory and visual signals requires the following phases: the first phase consists in the training of the acoustic and visual subnets; successively, the training data is passed through these two networks and two outputs (Aoutput and Voutput in Figure 16) are produced; finally, the outputs of the sub-networks are concatenated and this new data is used to train the late integration network. As a high amount of errors can exist in these three networks, two more networks are applied to predict errors of each sub-networks. The general architecture of the system proposed by Lewis et al. is shown in Figure 16. The main advantages of neural networks rely on their generalisability, the unnecessarity of assumptions about the underlying data and poor susceptibility to noise. However, this methodology is characterized by a slow trainability and variance due to rate.

CONClUsION In this chapter we have analyzed some fundamental features of multimodal fusion systems. A first important characteristic is the level at which the input signals are combined. In this respect we have shown that multimodal fusion strategies can be broadly classified as: (i) recognition-based, characterized by a unimodal recognition stage and an integrated decision stage; (ii) decision-based, where a unimodal recognition and interpretation phases are carried out before performing the integration of the extracted semantic meaning; (iii) hybrid multi-level, based on a distributed integration of input signals among the acquisition, recognition and decision levels. Secondly, we have briefly analysed the main mathematical methods

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Figure 15. The MS-TDNN network of Meier et al. (2000)

Figure 16. The fusion approach proposed by Lewis et al. (2002)

©2002 Australian Computer Society. Used with permission.

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applied to perform the integration of multimodal inputs, with a specific focus on statistical and artificial intelligence techniques.

REFERENCEs Andre, M., Popescu, V.G., Shaikh, A., Medl, A., Marsic, I., Kulikowski, C., & Flanagan J.L. (1998, January). Integration of speech and gesture for multimodal human-computer interaction. In Second International Conference on Cooperative Multimodal Communication, Tilburg, The Netherlands (pp. 28-30). Bolt, R. (1980). Put-that-there: Voice and gesture at the graphics interface. Computer Graphics, 14(3), 262-270. Bouchet, J., Nigay, L., & Ganille, T. (2004). Icare software components for rapidly developing multimodal interfaces. In Proceedings of the 6th International Conference on Multimodal Interfaces (ICMI ‘04),New York, NY (pp. 251258). ACM. Carpenter, B. (1992). The logic of typed feature structures. UK: Cambridge University Press. Chu, S.M., Libal, V., Marcheret, E., Neti, C., & Potamianos, G. (2004, June 27-30). Multistage information fusion for audio-visual speech recognition. In Proceedings of the (IEEE) International Conference on Multimedia and Expo 2004, (ICME ’04), New York, NY (Vol. 3, pp.1651-1654). Cohen, P.R., Johnston, M., McGee, D., Oviatt, S.L., Pittman, J., Smith, I.A., Chen, L., & Clow, J. (1997). Quickset: Multimodal interaction for distributed applications. ACM Multimedia, 31-40. Corradini, A., Mehta, M., Bernsen, N.O., & Martin, J.-C. (2003). Multimodal input fusion in human-computer interaction on the example of the ongoing NICE project. In Proceedings of the NATO-ASI Conference on Data Fusion for Situ-

ation Monitoring, Incident Detection, Alert, and Response Management, Yerevan, Armenia. Coutaz, J., & Caelen, J. (1991, November). A taxonomy for multimedia and multimodal user interfaces. In Proceedings of the 1st ERCIM Workshop on Multimedia HCI, Lisbon. D’Ulizia, A., Ferri, F., & Grifoni, P. (2007, November 25-30). A hybrid grammar-based approach to multimodal languages specification. In OTM 2007 Workshop Proceedings, Vilamoura, Portugal (LNCS 4805, pp. 367-376). Springer-Verlag. Gaitanis K., Vybornova, M.O., Gemo, M., & Macq, B. (2007). Multimodal high level fusion of input commands as a semantic goal-oriented cooperative process. 12th International Conference “Speech and Computer” (SPECOM 2007). Garg, A., Pavlovic, V., & Rehg, J.M. (2003). Boosted learning in dynamic bayesian networks for multimodal speaker detection. In Proceedings IEEE (No. 9, pp.1355-1369). Johnston, M. (1998, August 10-14). Unificationbased multimodal parsing. In Proceedings of the 36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics (COLINGACL ‘98), Montreal, Canada (pp. 624-630). Johnston, M., & Bangalore, S. (2000). Finitestate multimodal parsing and understanding. In Proceedings of the International Conference on Computational Linguistics, Saarbruecken, Germany. Lau, C.W., Ma, B., Meng, H.M., Moon, Y.S., & Yam, Y. (2004). Fuzzy logic decision fusion in a multimodal biometric system. In Proceedings of the 8th International Conference on Spoken Language Processing (ICSLP), Korea. Lewis, T.W., & Powers, D.M.W. (2002). Audiovisual speech recognition using red exclusion and neural networks. In Proceedings of the 25th

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Australasian Conference on Computer Science. Australian Computer Society, Inc., Melbourne, Australia (pp. 149-156). Martin, J.C., Grimard, S., & Alexandri, K. (2001). On the annotation of the multimodal behavior and computation of cooperation between modalities. In Proceedings of the Workshop on Representing, Annotating, and Evaluating Non-Verbal and Verbal Communicative Acts to Achieve Contextual Embodied Agents, Montreal, Canada (pp. 1-7). Meier, U., Stiefelhagen, R., Yang, J., & Waibel, A. (2000). Towards unrestricted lip reading. International Journal of Pattern Recognition and Artificial Intelligence, 14(5), 571-585. Neal, J.G., & Shapiro, S.C. (1991). Intelligent multimedia interface technology. In J. Sullivan, & S. Tyler (Eds.), Intelligent user interfaces (pp. 11-43). New York: ACM Press. Nigay, L., & Coutaz, J. (1995). A generic platform for addressing the multimodal challenge. In Proceedings of the Conference on Human Factors in Computing Systems. ACM Press. Oviatt, S.L. (2002). Multimodal interfaces. In J. Jacko, & A. Sears (Eds.), Handbook of humancomputer interaction. New Jersey: Lawrence Erlbaum. Oviatt, S., & Cohen, P. (2000). Multimodal interfaces that process what comes naturally. Communications of the ACM, 43(3), 45-53. Oviatt, S., Cohen, P., Wu, L., Vergo, J., Duncan, L., Suhm, B., Bers, J., Holzman, T., Winograd, T., Landay, J., Larson, J., & Ferro, D. (2000). Designing the user interface for multimodal speech and gesture applications: State-of-the-art systems and research directions for 2000 and beyond. Oviatt, S., De Angeli, A., & Kuhn, K. (1997). Integration and synchronization of input modes during multimodal human-computer interaction. In Proceedings of the Conference on Human Fac-

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tors in Computing Systems (CHI’97), New York, NY (pp. 415-422). ACM Press. Pavlovic, V.I., Berry, G.A., & Huang, T.S. (1997). Integration of audio/visual information for use in human-computer intelligent interaction. In Proceedings of the 1997 International Conference on Image Processing (ICIP ‘97), (Vol. 1, pp. 121-124). Pérez, G., Amores, G., & Manchón, P. (2005). Two strategies for multimodal fusion. In Proceedings of Multimodal Interaction for the Visualization and Exploration of Scientific Data, Trento, Italy (pp. 26-32). Quesada, J. F., Torre, D., & Amores, G. (2000). Design of a natural command language dialogue system. Deliverable 3.2, Siridus Project. Russ, G., Sallans, B., & Hareter, H. (2005, June 20-23). Semantic based information fusion in a multimodal interface. International Conference on Human-Computer Interaction (HCI’05), Las Vegas, NV (pp. 94-100). Schomaker, L., Nijtmans, A.J., Camurri, F. Lavagetto, P., Morasso, C., Benoit, T., et al. (1995). A taxonomy of multimodal interaction in the human information processing system. Multimodal Integration for Advanced Multimedia Interfaces (MIAMI). ESPRIT III, Basic Research Project 8579. Sharma, R., Pavlovic, V.I., & Huang, T.S. (1998). Toward multimodal human-computer interface. In Proceedings of the IEEE, Special Issue on Multimedia Signal Processing, 86(5), 853-869. Sun, Y., Chen, F., Shi, Y.D., & Chung, V. (2006). A novel method for multisensory data fusion in multimodal human computer interaction. In Proceedings of the 20th Conference of the Computer-Human Interaction Special Interest Group (CHISIG) of Australia on Computer-Human Interaction: Design, Activities, Artefacts, and Environments, Sydney, Australia (pp. 401-404).


Toyama, K., & Horvitz, E. (2000). Bayesian modality fusion: Probabilistic integration of multiple vision algorithms for head tracking. In Proceedings of the Fourth Asian Conference on Computer Vision, Tapei, Taiwan.

KEy TERMs AND DEFINITIONs

Vo, M.T. (1998). A framework and toolkit for the construction of multimodal learning interfaces. Unpublished doctoral dissertation, Carnegie Mellon University, Pittsburgh, PA.

Human-Computer Interaction (HCI): Discipline concerned with the design, evaluation and implementation of interactive computing systems for human use and with the study of major phenomena surrounding them.

Vo, M.T., & Wood, C. (1996, May 7-10). Building an application framework for speech and pen input integration in multimodal learning interfaces. In Proceedings of the Acoustics, Speech, and Signal Processing (ICASSP’96), IEEE Computer Society (Vol. 6, pp. 3545-3548). Wahlster, W., Reithinger, N., & Blocher, A. (2001). SmartKom: Multimodal communication with a life-like character. In Proceedings of Eurospeech, Aalborg, Denmark. Zhihong, Z., Yuxiao, H., Ming, L., Yun, F., & Huang, T.S. (2006). Training combination strategy of multistream fused hidden Markov model for audio-visual affect recognition. In Proceedings of the 14th Annual ACM International Conference on Multimedia, Santa Barbara, CA (pp. 65-68).

Communication Modality: The medium or channel of communication that conveys information.

Multimodal Fission: The process of disaggregating outputs through the various channels in order to provide the user with consistent feedback. Multimodal Fusion: The process of integrating information from various input modalities and combining them into a complete command. Multimodal Interface: System that allows input and/or output to be conveyed over multiple channels such as speech, graphics, and gesture. Multimodal Interaction: Interaction with the virtual and physical environment through natural modes of communication. Multimodality: Quality of a system to allow more than one communication modality to be used during human-computer interaction.

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Chapter IV

An Efficient Unification-Based Multimodal Language Processor for Multimodal Input Fusion Fang Chen National ICT Australia, Australia & University of Sydney, Australia Yong Sun National ICT Australia, Australia & University of Sydney, Australia

ABsTRACT Multimodal user interaction technology aims at building natural and intuitive interfaces allowing a user to interact with computers in a way similar to human-to-human communication, for example, through speech and gestures. As a critical component in a multimodal user interface, multimodal input fusion explores ways to effectively derive the combined semantic interpretation of user inputs through multiple modalities. Based on state–of-the-art review on multimodal input fusion approaches, this chapter presents a novel approach to multimodal input fusion based on speech and gesture; or speech and eye tracking. It can also be applied for other input modalities and extended to more than two modalities. It is the first time that a powerful combinational categorical grammar is adopted in multimodal input fusion. The effectiveness of the approach has been validated through user experiments, which indicated a low polynomial computational complexity while parsing versatile multimodal input patterns. It is very useful for mobile context. Future trends in multimodal input fusion will be discussed at the end of this chapter.

INTRODUCTION TO MUlTIMODAl INPUT FUsION Multimodal interaction systems allow a user to interact with them by using his/her own natural communication modalities such as pen gesture,

text and spoken language, graphical marks etc. After speech and pen gesture were used as input modalities in some researches, other modalities, such as hand gesture and eye gaze, have been addressed as well. To understand a user’s meaning conveyed through his/her use of multiple


An Efficient Unification-Based Multimodal Language Processor for Multimodal Input Fusion

modalities, Multimodal Input Fusion (MMIF) integrates users’ input from different modalities and derives a semantic interpretation from them for a system to act upon. It is an area that deals with various input modalities and finds users’ meaning in a particular instance, application or task. With the development of multimodal interface on mobile devices, multimodal input fusion has to deal with computational complexity, besides the existing issues. The aim of MMIF is to: a. b. c.

Identify the combination of input modalities and multimodal input patterns; and Interpret both unimodal input and multimodal inputs; and Cope with modality alternation for one multimodal utterance and support modality combination (complementary) for one utterance.

There is an important distinction between the aims of MMIF: •

•

Combining complementary inputs from different modalities, for example, a user touching an object to be deleted on a touch screen and saying ‘delete’. Improving the robustness of signal recognition, for example audio-visual speech recognition, or combined gaze and speech input (Zhang, Imamiya, Go & Mao 2004).

The pre-request for successful MMIF includes: • •

•

Fine grained time-stamping of user’s multimodal input (beginning and end); and Parallel recognizers for a modality. Based on probabilistic methods, the recognition results from parallel recognizers can be used to produce n-best list; and Unified representation schema for output of all recognizers; and

•

Time sensitive recognition process.

On the other hand, a successful MMIF should be able to • • • •

Handle input signals that may or may not be temporally overlapped; and Disambiguate inputs among different modalities; and Deal with simultaneous and/or sequential multimodal inputs; and Distribute to allow more computational power.

In a mobile device with a multimodal application, speech can be used as the primary modality because traditional user interface peripherals may not be available or appropriate. The most common instance of such interfaces combines a visual modality (e.g. a display, keyboard, and mouse) with a voice modality (speech). Other modalities such as pen-based input may be used, as well. In the more recent mobile devices, modalities can involve global positioning systems (GPS), and mapping technologies, digital imaging, barcode scanning, radio frequency identification (RFID) and other newer, intelligent technologies. In this kind of applications, users have more options to express their meaning, and tend frequently to switch between interaction modalities owing to a number of external factors such as background noise. Because multiple modalities can be used together to express a meaning, multimodal inputs tend to be more concise and meaningful. For example, “from the corner of Edi Ave. and George ” Street” can be concisely expressed as “from with speech and a point gesture.

Types of MMIF As shown in Figure 1, in general, multimodal input fusion can occur at two levels:

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•

•

Feature level (also known as “early fusion”), integration at the signal/feature extraction level; and Semantic level (also known as “late fusion”) integration at the recognized symbol level

•

•

Early Fusion Early fusion integrates multimodal inputs after the feature extraction of each input mode. It belongs to signal processing domain which often looks at recognition of combined features. Feature level fusion is most common between modalities that are closely related together and can be correlated fairly closely. For example, in audio-visual speech recognition, speech input can be fused with corresponding lip movements on video input at the recognition stage. Feature level fusion can also be performed between unrelated modalities for oneoff multimodal inputs, for example, identifying a person based on his voice and face. This kind of fusion can be summarized as following: • •

It is usually based on multiple Hidden Markov models or temporal neural networks; It is ideal for closely coupled and synchronized input modalities, where both input channels provide corresponding information to one another;

The recognition of one modality channel can affect the recognition process in the corresponding modality; Associated problems with early fusion are modeling complexity, computational load and the large amount of training data required to build such a system. Till now, there are no freely available multimodal training corpora to use on such components.

Late Fusion In one multimodal turn, all multimodal inputs compose of a multimodal utterance which can express a certain meaning. The goal of late fusion is to find the meaning. Late fusion (semantic fusion) integrates symbols recognized from multimodal input signals. Being applied after best interpretation for each input mode is determined; it is adopted more in the computer science/human computer interaction domain for heterogeneous input modes. It integrates related (but complementary) inputs that occur sequentially or simultaneously. A good example is pen and speech input. The summarization of late fusion can be: •

It is for input modes that differ in type of content or processing time;

Figure 1. Two possibilities for MMIF ‘early’ fusion feature extraction

pattern matching

selecting best

Sensor 2

feature extraction

pattern matching

selecting best

feature extraction

pattern matching

...

...

Sensor 1

Sensor n

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‘late’ fusion

selecting best

semantic level processing (dialog management, output generation)


•

•

It involves individual recognizers for each modality and the results of the recognition are integrated complementarily; It is easy to scale up and adopt different modalities.

Both early fusion and late fusion can produce an n-best list of possibilities, from most confident to least confident at the feature or semantic level interpretations. We will focus on ‘late’ fusion or semantic fusion in this chapter.

MMIF Related Steps MMIF discussed in this chapter focuses on late fusion whose inputs are recognized signals. As shown in Figure 2, to derive a MMIF result, the following steps are needed, collecting captured signals, controlling and registering input devices, recognizing them, segmenting them to multimodal utterances and fusing out the semantic interpretation of the utterances. Collecting captured signals is done by input devices, which could be microphones, touch screen, keyboard, video or any environmental sensors. Registered input devices send out captured signals as soon as they are received.

The “Controlling and registering input devices” module receives real time input from each of the input devices. Each input is time-stamped with the time when it is recorded by the input device. It is important that this is done at the input device and NOT when it is received. This is because latency may switch the order in which the user provided each of the inputs and any information that may be gleaned about the time intersection between input streams is lost. All inputs are then translated to a format understood by the recognizers. This module also handles dynamic registration and deregistration of input devices, keeps track of their capabilities and assigns identifications. There may be more than one input device of the same type and this module may decide whether to consider all inputs or only selected inputs. The recognizing task is undertaken by recognizers. In a “Members-Teams-Committee” inspired design, two or more recognizers may be able to “team-up” and produce a combined n-best list of results in cases where the modalities are closely coupled in time such as lip-movement recognition and speech recognition. The teams that may be formed between recognizers are known a-priori. There may also be more than one recognizers for each type of input modality,

Figure 2. Steps to derive the semantic interpretation of a multimodal utterance Input Device1 Input Device2 Input Device3

Control and Regi

MMIF

n

Input Device4

… Input Device n

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each able to output a set of n-best results. The ‘best’ single and combined results are sent to the segmentation module. Recognizing results are discrete symbols. Because there is no punctuations in symbols recognized by recognizers, the multimodal input streams have to be segmented to multimodal utterances for follow-up process. That is the task of segmentation function. The function of segmentation is to determine the borders of a multimodal utterance in multimodal input stream. Temporal relationship among multimodal inputs are utilized in multimodal input segmentation. Some systems use a static time window; a temporal gap in multimodal input bigger than the time window is regarded as a border between multimodal utterances. To produce a more accurate result, others make use of other information in multimodal input as well. More accurate the multimodal utterance is segmented; more natural interaction can be achieved. An MMIF module combines recognized symbols from different modalities to present a coherent piece of semantic information for a system to act upon. It synchronizes input from multiple modalities, resolves cross-modal references and rank multiple interpretations. The output of an MMIF module is the semantic interpretations of a multimodal utterance. Although different steps related of MMIF have been introduced conceptually, some steps are merged together, others are simplified/ignored due to specific environments in practice. Most signal recognizers recognize signals from one modality, and generate uncertain recognition results, therefore, an MMIF module should be

able to deal with uncertain data and generate its conclusion.

Utilizing the Fusion Results Without being utilized, fusion results will be useless. As illustrated in Figure 3, after the possible semantic interpretations of a multimodal utterance are derived, they are sent to Dialog Management module. With the context information, a Dialog management module selects the most suitable one to control the Application Background. Conventionally, the semantic interpretation is the truth or satisfaction conditions of multimodal inputs, ignoring those aspects that are influenced by the context of its use. Dialog Management takes care about everything to do with the context, and to a large extent it is used to sweep up all the difficult aspects that are not considered in MMIF. The Dialog Management module may also access to a set of knowledge bases that include information about the user, their input history, the tasks that can be accomplished by the application etc.

Two Challenges to MMIF Though a multimodal user interaction system brings some advantages in usability, the MMIF is more complex than the unimodal input processing, such as the natural language processing. It deals with more challenges than those in unimodal processing. The first is about the linear order in temporal domain. The words of a sentence follow a fixed linear order. An atomic input unit from a

Figure 3. A conceptual framework to utilize MMIF results Multimodal Input Fusion

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Semantic interpretation

Dialog Management

Application Background


recognizer to the MMIF is referred as a symbol. A complete set of symbols recognized from multimodal input signals is termed as a multimodal utterance which is usually bounded by the user’s silence. In a multimodal utterance, the linear order of symbols from multiple modalities is variable, but the linear order of symbols from one modality is invariable. For example, when a traffic controller wants to divert traffic on a road, he/she issues a multimodal command “divert traffic on this road to this street” while pointing to a road and a street in a map with the cursor of his/her hand on a screen. The gestures may be issued before, in-between, or after speech input, but the street pointing gesture is always after the road pointing gesture. This problem definition is different from Rudzicz (2006a) and Johnston (1998), which presume multimodal inputs are permutable, no matter whether they are from one or multiple modalities. The second is about the semantic contribution. The word string of a sentence determines the semantic interpretation of the sentence in a grammar. In a multimodal utterance, the symbol string from one modality contributes partially to the utterance’s syntactic structure and semantic interpretation. The notation of a multimodal utterance is a speech symbol string followed by a colon and a gesture symbol string. For example, when a traffic controller wants to block traffic on a part of a road in front of a digital map displayed on a large screen, he/she issues a multimodal command by saying “block traffic on this road between this street and that street” and pointing the corresponding entities on the map “Campbell_ Road”, “Musgrave_Street” and “Loftus_Street”; the multimodal utterance representation is “block traffic on this road between this street and that street : Campbell_Road Musgrave_Street Loftus_Street”. In this example, we can only find the multimodal utterance is a verbal phrase with incomplete semantic interpretation “block traffic on a road between two streets” from speech input. A complete semantic interpretation can

be derived from neither speech input nor gesture input. They can only be integrated from inputs from both modalities.

Approaches to semantic MMIF To achieve MMIF, various approaches have been proposed. Some are based on finite-state machine. Others base on unification operation. At the beginning, unification-based fusion was proposed in Johnston et al. (1997) to process versatile multimodal utterance patterns. Later, a finite-state based approach (Johnston and Bangalore, 2000) emerged to overcome the computational complexity issue of unification-based approaches. Based on these two tracks, different approaches have been proposed to meet different requirements in applications and environments.

Unification-Based MMIF In the unification-based MMIF approach, the fusion module applies a unification operation on multimodal utterances according to a multimodal grammar. During unification operation, when two terms are unified, they are compared; • •

If one is a variable, another is a constant; the variable will be bound to the other one. When both of them are constants, if they are equal then unification succeeds, otherwise unification fails.

In this kind of approaches, multimodal utterances, intermediate results and fusion results are represented in Typed Feature Structures (TFS) (Carpenter, 1992). A TFS consists of two pieces of information. The first is a type. Every feature structure must have a type drawn from the inheritance hierarchy. The other kind of information specified by a feature structure is a finite, possibly empty, collection of feature/ value pairs. A feature value pair consists of a

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Figure 4. Feature structure for "M1A1 platoon"   type : m1a1  object :    create _ unit  echelon : platoon  unit  location : [ ]point 

Figure 5. Interpretation of a point gesture    xcoord : 95305  command location :     ycoord : 94365  point  

Figure 6. Multimodal interpretation of a utterance   type : m1a1  object :    echelon : platoon  unit  create _ unit     xcoord : 95305  location :     ycoord : 94365 point  

feature and a value, where the value is an atom or a feature structure. Unification of TFSs is an operation that determines the consistency of two structures according to a multimodal grammar. Consistent structures are combined to a single TFS. It can be found in many published works such as Johnston et al. (1997), Johnston (1998) and Holzapfel, Nickel & Stiefelhagen (2004). Considering a unification example from Johnston et al. (1997), a user says, “M1A1 platoon”, and gestures on a point on the screen. The speech and gesture inputs are assigned the feature structures in Figure 4 and Figure 5. And they are unified to the feature structure in Figure 6. In Kaiser et al. (2003) and Kaiser et al. (2004), the unification-base MMIF was as following. As shown in Figure 7, there are a Natural Language Parser (NLP) and a Multimodal Integrator (MI) to undertake the multimodal fusion task. The NLP maps each parsable speech string into a TFS. Once a gesture is received, its gesture hypotheses are converted into a TFS. MI unifies speech TFSs and gesture TFSs with an internal

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chart parser. This process generates single object TFSs of the type specified for the speech/gesture combination, which are subsequently enrolled in the chart. Whenever the chart’s agenda empties, the complete edges present on the chart become the multimodal n-best list, and the top member of this list is regarded as the multimodal fusion result. This kind of approaches can handle a versatile multimodal utterance styles because all symbols in an utterance are supposed to be permutable. For example, for the utterance “pick up it” as speech and a pointing gesture to specify the object, they will try not only the order “pick up ” but also all other orders, such as “up it ” and “it pick up ” to find the pick it semantic interpretation of it. However, it suffers from significant computational complexity (Johnston & Bangalore, 2000). Some unification-based approaches adapt Earley’s chart parser (Earley, 1970) to multimodal processing, but that shows a prohibitive exponential computational complexity. Most of these approaches assume there is


Figure 7. A Multimodal Interaction Architecture (Based on Kaiser et al., 2003) Speech Recognition A user attached with gesture trackers

Gesture Recognition

Text strings

Gestures

Natural Language Parser

3D Reference Resolution

Speech feature structures

Multimodal Integrator

Gesture, selected objects

Object locations

User side

System side

not a linear order in a multimodal utterance. The temporal order of multimodal inputs is ensured by temporal constrains in parsing process. But it does not provide a natural framework for combining probabilities of speech and gesture events (Johnston & Bangalore, 2000). Development of the multimodal grammar rules is a laborious process and requires significant understanding of integration technique. Unification-based approach has been enhanced in different aspects. A unification-based approach in mutual disambiguation of speech and penbased gesture which supports combinations of speech with a single gesture is discussed in Oviatt (1999). In Portillo, Garcia & Carredano (2006), a unification-based approach with probabilistic parsing is presented. However, it is not clear the computational complexity of the approach.

Finite-State Based Multimodal Input Fusion Finite-state based approach was adopted in Johnston & Bangalore (2000). In this approach, a finite state device is used to encode multimodal

3D Environment Proxy

Combined feature structures

integration patterns, the syntax of speech phrase, gesture unit and the semantic interpretation of these multimodal utterances. Johnston & Bangalore (2000) uses a multimodal context-free grammar to describe the inputs from speech and gesture. The terminals in the grammar consist of literals of the form W:G:M where W is the words from speech input, G is the input from gesture represented as atomic symbols, and M is their multimodal interpretation. The multimodal grammar specification is compiled into a finite-state device running on three information flow tapes. A tape represents either an input stream (W stream or G stream) or an output stream (M stream). There are two input streams for speech (W stream) and gesture (G stream) modalities respectively. The third one is the output stream (M stream) for the multimodal interpretation. The finite state device consists of states and transitions. A transition from one state to another is labeled with multimodal grammar denoting the inputs required in speech and/or gesture to perform the transition. Multimodal fusion is achieved by parsing spoken and gesture inputs using the finite state machine. During parsing, the transition from one state to

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another outputs the meaning M associated with that grammar fragment. The finite-state device receives W stream and G stream in the form of words and gesture symbols respectively and outputs their meaning according to the pre-defined multimodal grammar. The collection from the M stream for a parse through the multimodal inputs represents the multimodal interpretation of the multimodal utterance. Considering a fragment of the multimodal grammar shown in Figure 8, it allows a user to issue commands such as “email this person and that organization” and gesture on appropriate person and organization. In the grammar, the non-terminals in the multimodal grammar are atomic symbols. The multimodal aspects of the grammar become apparent in the terminals. Each terminal contains three components W:G:M corresponding to the 3 tapes, where W is for the spoken language stream, G is the gesture stream, and M is the combined meaning. The epsilon symbol is used to indicate when one of these is empty in a given terminal. The symbols in W are words from the speech stream. The symbols in G are of two types. Symbols like Go indicate the presence of a particular kind of gesture in the gesture stream, while those like e1 are used as references to entities referred to by the gesture. Simple deictic pointing gestures are assigned

semantic types based on the entities they are references to. Go represents a gesture reference to an organization on the display, Gp to a person, and Gd to a department. Figure 9 shows the three tape finite state device that is compiled based on the grammar. The above input is parsed to generate email([person(id1), org(id2)]) where id1 and id2 are the specific entities that are generated by the gesture. By assuming that a multimodal utterance has a certain order, this approach achieves efficient multimodal parsing. For example, for the utterance “pick up it” as speech and a pointing gesture to specify the object, the input order ”. However, this kind should be “pick up it of approaches does not support the permutation in inputs. The same utterance in a different order, ”, needs to be licensed by like“pick it up another rule in the context-free grammar. That will cause considerable categorial ambiguity when providing adequate coverage by the grammar. More importantly, there is no systematic relationship predicated between the multiple categories despite the fact that they prescribe the same meaning with alternative temporal orders. While three-tape finite state devices are feasible in principle (Rosenberg, 1964); current techniques only support two information flow tapes finite-state machine. Thus, the implementation of

Figure 8. A Multimodal Grammar (Based on Johnston and Bangalore, 2000) Speech string: Gesture string:

sm’+1 … sm

s1 s2 … sm’ g1 g2 … gn’

gn’+1 … gn

: the first sub-utterance : the second sub-utterance

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this approach requires converting three tapes finite state machine to two tapes finite state machine. This can be a significant issue when scaling to complex applications and/or more than two modalities. So, it is a light-weighted implementation which is suitable for small applications. Recently, in Latoschik (2005), a similar approach, which uses a modified temporal augmented transition network to integrate speech, gesture and context information in parallel, was reported. This kind of semantic fusion approach makes it possible to tightly couple multimodal fusion with speech and gesture recognition.

BACKGROUND AND PRElIMINARy NOTIONs Combinatory Categorial Grammar (CCG) (Steedman, 2000) grammar is adapted to define the

multimodal utterances in the MMIF approach introduced in next section. It is a form of lexicalized grammar in which the application of syntactic rules is entirely conditioned on the syntactic type (category). Categories identify constituents as either atomic categories or functions. Atomic categories, such as N, NP, S and so on, may be further distinguished by features, such as number, case etc. Functions (such as verbs) bear categories identifying the type of their results (such as verbal phrase) and that of their argument(s)/complement(s). Functions also define the order in which the arguments must combine, and whether they must occur to the right or the left of the function. Therefore CCG is mildly context-sensitive. It can describe multimodal utterances more sophisticatedly than a context-free grammar which is used in most of other unification-based MMIF approaches. Lexical entries are specified by pairing symbols with

Figure 9. A Finite state device for multimodal parsing (Based on Johnston and Bangalore, 2000) 0 1 2

email:eps:email([

1

this:_:_ OR

2

that:_:_

person:Gp:person( OR department:Gd:dept(

3

_:e1:e1 OR

3 4 5

_:e2:e2 OR _:_:)

4 5

_:_:])

6 eps:e1:e1

department:Gd:dept( that:eps:eps

0

email:eps:email([ page:eps:page([

this:eps:eps

1

2

organization:Go:org( person:Gp:person(

3

eps:e2:e2 eps:e3:e3

4

eps:eps:)

5

eps:eps:])

6

67


categories and their semantic interpretations. For example, the CCG lexicon in Figure 10 defines that “select” should follow a noun phrase (np) and precede an np, the final result is a sentence (s) after these arguments are ful-filled. It also defines “np/n” as the category of “the” and “n” as the category of “Macgregor_Street”. CCG also defines a set of rules to combine two categories and type-raise a category to another one, such as application rules, composition rules, crossing composition rules and type-raising rules (see Figure 11). The CCG parsing process of “select the Macgregor_Street” is illustrated in Figure 12. Firstly, three words are assigned categories defined in lexicon, respectively. Then, “the” is combined with “Macgregor_Street” to an “np” according to forward application rule. Finally, “select” is combined with “the Macgregor_Street” to “s\np” which is typically a verbal phrase which follows an np to form a sentence (s). Because of CCG’s semantic transparency, the semantic interpretation for an utterance is built compositionally and in parallel to the syntactic derivation. CYK (Younger, 1967) algorithm is used to apply the rules in CCG to multimodal utterances. The parsing algorithm

Figure 10. A CCG lexicon example select :- s\np/np the :- np/n Macgregor_Street : - n

proposed later borrows the notion of CYK algorithm. We will focus on syntactic derivation of a multimodal utterance firstly and address the semantic interpretation later in next sections.

POlyNOMIAl UNIFICATION-BAsED MUlTIMODAl PARsING PROCEssOR Multimodal input fusion on a mobile device requires less computational complexity. To achieve a low polynomial computational complexity while parsing multimodal inputs in versatile styles, a fusion approach termed as Polynomial Unification-based Multimodal Parsing Processor (PUMPP) (Sun, Shi, Chen & Chung, 2007b) was proposed recently. Different from other fusion approaches, it adopts CCG to define multimodal utterances and specify their syntactic structures and semantic interpretations. CCG provides a mildly context sensitivity while the grammars in other approaches are context-free. CCG also has more generative power than a context-free grammar. The permutation of multimodal inputs can be specified in the grammar rather than supposed by parsing algorithms. The multimodal grammar developers pay less attention to implementation in this approach than in previous unification-based approaches. An algorithm is designed to process parallel multimodal inputs while keeping a low computational complexity. With the principle in the algorithm, it can be easily extended to handle

Figure 11. CCG rules

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Forward Application (>):

X/Y Y => X

Backward Application ( X

Forward Composition (>B):

X/Y Y/Z => X/Z

Backward Composition ( X\Z

Forward Type-raising (>T):

X => T/(T\X)


Figure 12. A CCG parsing example select

the

Macgregor_Street

--------

---

-----------------

s\np/np

np/n

n

Figure 13. A multimodal utterance and its sub-utterances Speech string: Gesture string:

sm’+1 … sm

s1 s2 … sm’ g1 g2 … gn’

gn’+1 … gn

: the first sub-utterance : the second sub-utterance

inputs from more than two modalities. With the semantically complementary relationship, it can also disambiguate recognized multimodal inputs. It has been successfully applied in applications with speech; hand gesture and eye gaze as input modalities. The main novelties of the approach over traditional unification-based approach lie in these aspects: •

•

•

The parsing algorithm can handle two parallel input strings while maintaining the low polynomial computational complexity. CCG grammar is adapted to define multimodal utterances and specify their syntactic structures and semantic interpretations. That provides a mildly context sensitivity, and the grammars in most of other unification-based approaches are context-free. The multimodal inputs are parsed symbol by symbol rather than phrase by phrase as in other unification-based MMIF approaches.

Basic Principle in PUMPP The parsing result of a multimodal utterance is the sequential composition of parsing results of its sub-utterances. In Figure 13, a multimodal utterance “s1 s2 … sm : g1 g2 … gn” composes of two sub-utterances. The first one covers speech symbols from s1 to sm’ and gesture symbols from g1 to gn’. The second one covers speech symbols from sm’+1 to sm and gesture symbols from gn’+1 to gn. The parsing result of the utterance is the sequential composition of parsing results of these two. With this principle, PUMPP will never try to combine “s3 : g2” with “s1 : g3”, because the speech inputs are not in temporal order. Therefore, PUMPP supposes the temporal order between symbols from one modality in a multimodal utterance is fixed.

Architecture of the PUMPP The architecture of the PUMPP is illustrated in Figure 14. It is made up of two elements. The

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Figure 14. The architecture of the PUMPP Symbol string 1 PUMPP parser

structure and

Symbol string 2

grammar

multimodal CCG grammar defines multimodal utterance styles and semantic interpretation. The PUMPP parser applies the CCG rules to categories of symbols of a multimodal utterance. The symbol strings are received and processed by the PUMPP parser symbol by symbol. This processing style enables incremental parsing between symbols from different modalities. The output of the parser is all possible syntactic structures and the semantic interpretations of the multimodal utterance which can be used for pragmatic processing in a specific application. For PUMPP, speech input is represented as a string of words, which are referred as speech symbols. However, the representation of gesture inputs needs some discussions. We classify gesture input of the processor into two categories, expressive gesture and deictic gesture. An expressive gesture can indicate a specific meaning independent of the context information. For example, a user holds his/her fist to mean “mark somewhere as an accident site”. An expressive gesture can be represented with a symbol which is assigned the meaning of it. On the other hand, deictic gestures can only be understood in a context within which it is issued. For example, when a user points to an entity in a context, what he/she wants to refer to is the entity rather than the coordinate of his/her hand cursor. Therefore, there is a task to recognize the entities referred to by deictic gestures from their coordinates within a specific context. If the task is accomplished right after gesture recognition, the efficiency of the multimodal system can be

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improved in a certain degree. For PUMPP, the recognized symbol for a deictic gesture represents the entity it referred to. For example, a pointing gesture pointing to “River Terrace” is represented with symbol “River_Terrace”.

Algorithm in the PUMPP The algorithm in PUMPP is based on CYK algorithm designed for natural language processing, a kind of unimodal processing. CYK algorithm uses a simple two-dimensional matrix to record the constituents in parsing process. Based on its principle, we designed the matrix to record constituents and the loops to calculate the constituents in parsing parallel symbol strings.

Matrix to Record Parsing Constituents For a sentence of length n, the CYK algorithm works with the upper-triangular portion of an n×n matrix. To accommodate the parsing constituents of two parallel input symbol strings, the twodimensional matrix in original CYK algorithm is replaced by a four-dimensional matrix. For a multimodal utterance “A B C : x y”, the matrix to record parsing constituents is illustrated in Figure 15. The first pair of index of a cell marks the speech span of the sub-utterance and the second marks the gesture span of the sub-utterance. The cell(1,3;0,2) records the parsing results for the sub-utterance (BC:xy) spanning from 1 to 3 for speech symbols (BC) and from 0 to 2 for gesture symbols (xy). The first row of the matrix contains


Figure 15. The matrix for the multimodal utterance "A B C : x y " in PUMPP A multimodal utterance bounded by integers: 0

A

1

B

Extended Matrix: (speech span) 0,0

2

C

3

0,0

0,1 A (0,1;0,0)

:

0

x

1

y

2

1,2 0,2 B AB (1,2;0,0) (0,2;0,0)

2,3 C (2,3;0,0)

0,1 1,2 (gesture span) 0,2

1,3 BC (1,3;0,0)

0,3 ABC (0,3;0,0)

BC:xy (1,3;0,2)

ABC:xy (0,3;0,2)

x (0,0;0,1) y (0,0;1,2)

B:y (1,2,1,2)

xy (0,0:0,2)

Figure 16. Flow-chart to calculate the constituents in a cell for a multimodal sub-utterance in the algorithm of PUMPP start sm := ss+1 sm < se

n

y gm := gs+1 gm < ge

n

y For every element in cell(ss,sm;gs,gm), B, and every element in cell(sm,se;gm,ge), C, If: B combine C => A, Then: push A to cell(ss,se;gs,ge) gm := gm+1 sm := sm+1 end

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the parsing results of sub-utterances which do not have gesture symbol strings (gesture spans from 0 to 0). The first column of the matrix contains the parsing results for gesture only sub-utterances. The parsing results for the whole utterance are kept in the bottom-right cell (0,3;0,2). In practice, a function is used to map an index pair to an integer. The two pairs of integer indices of a cell are converted to two integer indices. Therefore, the four-dimensional matrix is flattened to a twodimensional matrix.

Calculating the Constituents in a Cell of the Matrix In the CYK algorithm, the constituents for a subsentence spanning i to j are calculated from all sub-sentence pairs which can concatenate to the sub-sentence. In the PUMPP parsing algorithm, the constituents for a sub-utterance spanning ss to se in speech and gs to ge in gesture are calculated from all sub-utterance pairs whose speech concatenations cover from ss to se and gesture concatenations cover from gs to ge (see Figure 16).

Extending the PUMPP Algorithm to Handle More Than 2 Modalities This algorithm can be easily extended to handle n modalities with the following principles while keeping polynomial computational complexity: • •

•

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Using n-dimensional matrix to record parsing constituents; Making the cell for a sub-utterance spanning modality x from mxs to mxe (x ranges from 1 to n) as cell(m1s, m1e; m2s, m2e; … ; mns, m ne); Adding 3 nested loops for each modality; two for looping through the cells in the matrix, one for calculating the content in a cell.

Discussion About the Algorithm The PUMPP algorithm can parse two parallel input symbol strings which are speech and gesture in the experiment. The basic insight behind this algorithm is that analyses of shorter sub-utterances can be recorded in the matrix and reused for the analyses of increasingly larger sub-utterances, which leads to polynomial recognition times instead of exponential in the length of a multimodal utterance. Flattening a four-dimensional matrix to a two-dimensional facilitates the algorithm to be understood and implemented. The CYK algorithm has worst-case computational complexity n3 for recognizing a sentence with n words in the context-free case. Its worst-case computational complexity is n6 for Vijay-Shanker and Weir (1990) generalization to CCG with a complex structuresharing technique for categories. The proposed algorithm adds three more nested loops due to the second modality, and its computational complexity is still polynomial. Although no experiment with the proposed parser has been done on a real set of multimodal utterances, the experiments by Komagata (Komagata, 1997; Komagate, 1999) with a CYK parser for hand-build CCG grammar fragments of English and Japanese for real texts from a constrained medical domain suggest that average-case parsing complexity for practical CCG-based grammars can be quite reasonable in practice. This improvement overcomes the drawback of unification-based MMIF approaches in computational complexity claimed in (Johnston & Bangalore, 2005).

Disambiguating Speech Recognition Results with PUMPP With the complementary relationship between the speech symbol string and the gesture symbol string of an utterance, PUMPP can disambiguate speech recognition results. Due to ambient noises, speaker variations and many other mismatches, some ASR systems provide several possible


recognition symbol strings rather than a certain one. If the most possible one is what a user wants to say, speech recognition by ASR is regarded as successful. In practice, limited deictic gesture and a few types of expressive gestures result in virtually errorless gesture recognitions. PUMPP sequentially tries to integrate possible speech symbol strings with the recognized gesture symbol string until a parsing result can be derived. If the possible speech symbol string PUMPP stops at is not the most possible speech symbol string, but it is what a user wants to say; PUMPP successfully disambiguates speech recognition results.

Implementation of PUMPP The implementation of PUMPP is based on openCCG (Baldridge, Kruijff & White, 2003) which is designed to parse sentences with CCG grammars. One of the most straightforward extensions is to add a multimodal parsing package opennlp.ccg.mparse based on package opennlp. ccg.parse. The CYK parsing algorithm was replaced by the PUMPP algorithm. The matrix was changed from a square whose size is the length of a sentence with a rectangular whose

width and depth are determined by the lengths of speech symbols and gesture symbols. The implementation of category structure, CCG rules, semantic structure and grammar loading facility in openCCG are kept.

Understanding the PUMPP Results with a Dialog Management Module Without being utilized, PUMPP result will be useless. It is useful to discuss how to understand PUMPP result in a system. With the semantic interpretation and the discourse information, a Dialog management module can control an application background. Here, we use a multimodal presentation system, Multimodal Presentation Markup Language 3D (MPML3D) player, as an example to explain the mechanism. As shown in Figure 17, The MPML3D player acts as a combination of multimodal input pre-processing module, dialog management module and application background. It accepts the multimodal presentation content from the content generation module; and delivers the specified multimodal presentation to a user. During the presentation, it also listens to the signals captured by an eye

Figure 17. The structure of a multimodal presentation system PUMPP multimodal input fusion Multimodal utterance

Syntactic structure and semantic interpretation

Multimodal presentation scripts in MPML3D and discourse information of the scripts







MPML3D multimodal presentation player 



Eye gaze

Speech

Eye tracker

Mic

Presentation

 User

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Figure 18. The input and output of THE HINGE Syntactic structure Discourse and information semantic interpretation

THE HINGE Which clip to present MPML3D multimodal presentation player

Figure 19. Three feature structures

[Agreement [Person

3rd ]]

[Agreement [Number   Agreement 

single]]

 Person 3rd   Number single    

tracker and a Mic. The captured eye fixations are interpreted to the entities being fixated on. A speech recognizer attached to the MPML3D player recognizes the captured speech signal to speech strings. The MPML3D player constructs a multimodal utterance with these information, and passes it to the PUMPP multimodal input fusion module which returns all possible syntactic structures and semantic interpretations of the utterance as the fusion result. With the semantic interpretation and the discourse information, THE HINGE, which is the component in MPML3D player to utilize MMIF results, decides the content to deliver to a user in the next step. Figure 18 highlights the input and output of THE HINGE. Specifically, they are the PUMPP result and the presenting decision. In the discourse information, each presentation clip is assigned a triggering condition. Syntactic structure, one part of PUMPP result, is represented in feature structure, and another part of PUMPP result, semantic interpretation, is represented in hybrid logic. For a dialog management module, semantic

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(1) (2)

(3)

interpretation of a multimodal utterance is more important than its syntactic structure.

Subsumption in Hybrid Logic Feature Structures and Subsumption Feature structures were used in some of previous researches to represent semantic interpretation. They are simply sets of attribute-value pairs, where attributes are atomic symbols, and values are either atomic symbols or feature structures themselves. In Figure 19, feature structure (1) has an attribute “Agreement” whose value is another feature structure, which has an attribute “Person” and value “3rd”. As introduced in previous section “Unificationbased MMIF”, when two feature structures are unified, they are compared with every attribute in anyone of them. (1) If one’s value is a variable or not specified, another’s value is a constant; the variable will be bound to the other one. (2) When both of them have constant values; if their values are equal then unification succeeds, otherwise


unification fails. The result of unification is two identical feature structures or fail. In Figure 19, the unification result of (1) and (2) is (3). Intuitively, unifying two feature structures produces a new one which is more specific (having more attributes) than, or is identical to, either of the input feature structures. We say a more general feature structure subsumes an equally or more specific one. In Figure 19, both (1) and (2) subsume (3). If feature structure A subsumes B, B satisfies all constraints outlined by A. Although feature structures can be used to represent semantic interpretation, hybrid logic is more suitable to capture meaning. More information about feature structure and unification operation can be found at Jurafsky & Martin (2008).

Hybrid Logic and Feature Structures A hybrid logic (Baldridge & Kruijff, 2002) formula can be viewed as a flat conjunction of the heads and dependents inside it. Hybrid logic provides an internal means to refer to propositions. A hybrid logic formula can be formulized as: head dependent1 dependent2… A dependent can be one of the following: • • •

Nominal: Proposition Nominal: Proposition Nominal: Nominal2

 Nominal2 is another nominal which refers other dependents. For example, the formula in Figure 20 is a hybrid logic formula. Its head is m1. The dependents specify the value of the head and its properties. “s1:proposition” specifies that the m1 has an attribute “HasProp” whose value is a nominal s1. Although feature structures are essentially a two-dimensional notation for hybrid logic (Blackburn 2000), by making use of nominals, hybrid logic allows adjuncts to insert their semantic import into the meaning of the head. This flexibility makes it amenable to represent a wide variety of semantic phenomena in a propositional setting, and it can furthermore be used to formulate a discourse theory (Baldridge & Kruijff, 2002). The track from grammar to discourse can be covered with a single meaning formalism. That is one of the reasons why we chose hybrid logic to represent semantic interpretation of multimodal utterances and discourse information.

Hinging Semantic Interpretation of Multimodal Utterances and Multimodal Presentations To determine the clip in corresponding with a multimodal utterance, every clip is assigned a triggering condition in discourse information, which is the loosest semantic interpretation to select this clip. For example, for the clip “Flash memory is the memory medium of this EasyMP-

Figure 20. A hybrid logic formula @m1 (@s1:proposition(storage) ^ @m1:phys-obj(medium) ^ @m1:phys-obj(sg) ^ @m1:phys-obj(s1:proposition))

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Figure 21. A triggering condition in hybrid logic. @q (@q:quantification(what) ^ @q:quantification(e:state) ^ @e:state(be) ^ @e:state(x:phys-obj) ^ @x:phys-obj(medium) ^ @x:phys-obj(m) ^ @m(y:appliance) ^ @y:appliance(EasyMP3Pod))

Figure 22. The Flowchart for “subsume(Head of Tdep, Head of Sutterance)”. Return true

start

Return false

N

Head of Tclip has another dependent Tdep Y The value of Tdep, Value(T dep) is a proposition

N

Y Y

The value of Tdep, N Value(T dep) is a modality as Mod1, Valuemod Y Sutterance’s head has a dependent whose value is Mod1, Valueutterance

The value of Sutterance’s head equals value(Tdep) N

N

Y

N

N

Valuemod is a nominal

Valuemod is a proposition Y

Y Valueutterance is a nominal Y

N

Valuemod equals Valueutterance

N

Y

subsume(Valuemod,Valueutterance)

N

Y

3Pod”, the triggering condition in Figure 21 is defined. It subsumes the semantic interpretation of multimodal utterances such as “what is the storage medium of this easy one” while fixating on “EasyMP3Pod”, “what is the memory medium of this easy one” while fixating on “EasyMP3Pod”, and so on. To verify if the semantic interpretation of a multimodal utterance (Sutterance) satisfies the triggering condition of a clip (Tclip), THE HINGE 76

checks if Tclip subsumes Sutterance with the process described in Figure 22.

ExPERIMENTs In this section, we will describe several experiments conducted to verify different aspects of PUMPP.


Table 1. Summarization of the multimodal utterances in the experiment Item

Total

speech length 5

√

deictic gesture length = 1

√

√

√

√


√

√

√


√

with expressive gesture

√

√

√

√

Number of multimodal utterances

6

29

1

5

1

1

3

3

49

% to whole multimodal utterance set

12.24

59.18

2.04

10.20

2.04

2.04

6.12

6.12

100.00

Table 2. Summarization of multimodal utterances and their parsing results Category

Number of parsing results per utterance 0

2

4.08

2

1

38

77.55

3

2

4

8.16

4

3

1

2.04

5

10

1

2.04

6

11

3

6.12

49

100.00

Parsing Two Parallel Symbol Strings with PUMPP This experiment was designed and conducted to verify:

2.

%

1

Total

1.

Number of multimodal utterances

If the PUMPP can handle two parallel input symbol strings; and The number of parsing results generated by the PUMPP for a multimodal utterance.

Multimodal Utterance Set Multimodal utterances in the experiment were collected and selected from previous user study (Chen et al., 2005; Ruiz, Taib & Chen, 2006) about map-based traffic incident management,

such as “zoom in” with a gesture point to the top-left quadrant of a map and “the interaction of State_Circle and Brisbane_Avenue” with a hand shape gesture to indicate “mark … as an incident site”. Table 1 is a summarization of the multimodal utterances in this experiment. In the multimodal utterance set, there were 49 multimodal utterances with 91 words for speech and 50 symbols for gesture. Symbol length of an utterance ranged from 3 to 14. Five types of expressive gesture were included in the lexicon.

Preliminary Results and Analysis Because multimodal utterances used in the experiment are more tool-oriented, most of the

77


expected parsing results are verbal phrases which are imperative. From Table 2, we can see the following: 1.

2. 3.

4.

The proposed processor can parse most utterances, except 2 multimodal utterances (Category 1, 4.08%); Most utterances(Category 2, 77.55%) derive a unique parsing result; About 10% (Category 3&4) utterances have slight parsing ambiguity in parsing results; and About 8% (Category 5&6) utterances result in significant parsing ambiguity.

According to our analysis, there are mainly three reasons causing parsing ambiguities. They are (1) the permutation between multimodal inputs from different modalities; (2) unnecessary type-raising and (3) multiple prepositional phrases decorating a noun.

One of the multimodal utterances which has ambiguity in parsing results is “Pitt_Street and Market_Street : accident”. Three of its parsing results are shown in Figure 23. In parsing result 1 of Figure 23, “accident :- n/n” is combined with “Market_Street :- n” to n. In parsing result 2 of Figure 23, “accident :- n/n” is combined with “Pitt_Street :- n” to n. In parsing result 3 of Figure 23, “accident :- s\none_np/np” combines with “Pitt_Street and Market_Street :- np” to s\none_np. This observation demonstrates the PUMPP algorithm tries different combination possibilities while keeping the order of symbols in one modality. It also demonstrates that the variable linear order of multimodal symbols from different modalities is a cause of parsing ambiguity although the input from one modality may screen out inappropriate symbols from the other modality. Applying type-raising rule to generate a new category from an existing category facilitates com-

Figure 23. Three parsing results of an ambiguous multimodal utterance due to modality permutation #

1

2

3

78

Parsing Result Pitt_Street and accident Market_Street ----------- ---------- ------------n np\n/n n/n n ----------------------> n ----------------------------< np\n -----------------------------------------> np accident Pitt_Street and Market_Street -------- ----------- --------------n/n n np\n/n n --------------------> n -------------------> np\n -----------------------------------------< np accident Pitt_Street and Market_Street -------- ----------- --- ------------s\none_np/np n np\n/n n ------------------> np\n -------------------------------< np ------------------------------------------> s\none_np


position of categories, and it also over-generates categories when there is no further composition. For example, in Figure 24, after speech phrase “the corner of Bell_Avenue and River_Terrace” derives an “np” as parsing result 1, it is applied forward and backward type-raising in parsing result 2 and parsing result 3 in Figure 24. Another two more parsing results are derived. When there are two prepositional phrases decorating a noun, the PUMPP generates all possible results without any common sense. For example, in Figure 25, “between White_River and South_Split” decorates “Lytton_Road” in parsing result 1, then “on Lytton_Road between White_River and South_Split” decorates “directions”. In parsing result 2 of Figure 25, first, “on Lytton_Road” decorates “directions”; second,

“between White_River and South_Split” decorates “directions on Lytton_Road”. To clarify parsing ambiguities, parsing results can be chosen by the dialog management module based on the context information, and probabilistic parsing can be introduced into parsing algorithm as well. The computational complexity of PUMPP has been discussed previously. In this experiment, the parsing time ranges from 0ms to 271ms. The utterance with the longest parsing time has 12 symbols from speech, 2 symbols from gesture and 10 parsing results. That means the multimodal parsing has very little impact on the system reaction time, especially in an interactive process in which the parsing process is triggered as soon as a multimodal symbol is received.

Figure 24. Three parsing results of an ambiguous multimodal utterance due to type-raising # 1 2 3

Parsing Result the corner of Bell_Avenue and River_Terrace -------------------------------------------np -----------------------------------------T s/@i(s\@inp)

Figure 25. Two parsing results of a noun with two decorative prepositional phrases #

1

2

Parsing Result directions on Lytton_Road between White_River and South_Split -------- --- ----------- ----------------------------------n n\n/n n n\n ----------------------------------------------< n -------------------------------------------------> n\n ------------------------------------------------------------< n directions on Lytton_Road between White_River and South_Split ---------- -------------- ----------------------------------n n\n n\n -------------------------< n ------------------------------------------------------------< n

79


Disambiguating Speech Recognition Results with Gesture Recognition Results To verify the ability of PUMPP to disambiguate speech recognition results, an experiment is conducted.

Experiment Setup In the experiment, there were 10 subjects, 8 males and 2 females, aging from 20 to 40. All of them are native English speakers. The multimodal CCG (MCCG) grammar and multimodal utterances were same as those in experiment “Parsing two Parallel Strings with PUMPP”. Forty-nine speech phrases which are legitimate to the MCCG were pre-scripted for subjects to read. Their voice was recorded by Microsoft Sound Recorder with a general Mic. In case of unexpected mistakes, the subjects were required to read the phrases twice, but only one voice track was used. For a public multimodal command-control interface, it is cumbersome to calibrate an ASR with every user to improve speech recognition accuracy. Therefore, a neural network speakerindependent ASR (Fonix VoiceIn 4.0, www. fonix.com) was used in this experiment. All possible phrases were defined in Fonix Grammar nodes. As illustrated in Figure 26, the Mic of PC2 captures speech signals replayed by PC1. The speaker-independent ASR running on PC2 recognizes speech signals to a 4-Best list. Gesture inputs to PUMPP are pre-computed to simulate errorless gesture recognition. PUMPP is running

on PC2 to accept the 4-Best list. Both the 4-Best lists and PUMPP results were logged. The prescripted speech phrases were regarded as what a user wants to say.

Experiment Result and Analysis Let NASR be the number of successful speech recognition by ASR, N PUMPP be the number of speech recognition successfully disambiguated by PUMPP, and then NASR,PUMPP is defined as NASR plus NPUMPP. Table 3 summarizes the experiment result. We use the NASR and NASR,PUMPP as two groups of data for a paired t test. The test result is t(9)=2.262, p 日本語にほんご

Figure 8. SVG markup for "Hello, out there" Example text01 - 'Hello, out there' in black Hello, out there

Another possibility for a standard approach to the visual modality is Scalable Vector Graphics (SVG)(Ferraiolo, Fujisawa, & Jackson, 2003). SVG is a markup for vector graphics, and it can be used to define displays of graphical elements, text and animations. SVG can be displayed in many browsers, or in standalone applications and can be combined with HTML. Ferraiolo, Fuijisawa, & Jackson, 2003. shows the SVG markup for the text “Hello, out there”, rendered in Figure 9.

422

Building New Modality Components A very interesting feature of the MMI architecture results from the fact that the API between the RF and the MC’s is generically defined as consisting of the life cycle events and EMMA. This means that to define a new type of modality component, an implementer has a fairly well-defined task; that is, to specify any modality-specific extensionNotification events and/or data fields required by

Standards for Multimodal Interaction

Figure 9. Rendered SVG

the modality. This may be a complex task, but in many cases API’s to modality components may already exist that can form the basis for defining a modality component. For example, for speech components, API’s like the Java Speech API (JSAPI)(“Java Speech API,” 1998), the Microsoft Speech API (SAPI) (Microsoft, 2007) and MRCP (Shanmugham & Burnett, 2008) have already been defined. Instructions for defining modality components and an example are included in the MMI Architecture specification (Barnett, Dahl et al., 2008)

ThE MUlTIMODAl UsER INTERFACE As mentioned above, recommendations for user interfaces tend to be best practices or guidelines rather than strict standards. They can be more flexible because users are more able to deal with variation than software. It is often difficult to give hard and fast rules for applications that is are complex as a multimodal user interface. (Larson, 2006) addresses the topic of multimodal user interfaces. This paper presents four major principles of multimodal user interaction. 1. 2.

3.

Satisfy real-world constraints (for example, if the user’s hands are busy, use speech) Communicate clearly, concisely, and consistently with users (for example, phrase all prompts consistently) Help users recover quickly and efficiently from errors (for example, present words recognized by the speech recognition system on the display)

4.

Make users comfortable (for example, always present the current system status to the user).

Accessibility is often mentioned as a motivation for multimodal applications. While not dealing specifically with multimodal applications, (Caldwell et al., 2007) presents good general accessibility guidelines that are often relevant to multimodal applications. Another commonly mentioned motivation for multimodality is the small screens and keypads found on today’s mobile devices make GUI interaction difficult. The Mobile Web Best Practices Working Group at the W3C has prepared a specification on best user interface practices for the mobile web (Rabin & McCathieNevile, 2006), focusing on the GUI interface.

CERTIFICATION sTANDARDs There are few certification programs that are relevant to multimodality, largely because the field is relatively new. We will briefly mention two certifications here. For certifying that an application’s user interface is appropriate for mobile devices, (Owen & Rabin, 2007) provides tests for certifying that an application is “MobileOK”, based on the Mobile Web Best Practices specification (Rabin & McCathieNevile, 2006) described in Section 0. The VoiceXML Forum (www.voicexmlforum. org) has developed programs for certifying platforms as VoiceXML compliant and for certification of VoiceXML developers.

423


PUTTING IT All TOGEThER: A VOICE sEARCh APPlICATION As an example of putting standards together to create applications, we can consider a multimodal voice search application. This kind of application would enable the user to ask for a particular type of information, for example, “Where’s the nearest parking garage?”, or “Where’s the nearest Italian restaurant?” This is a good multimodal application in mobile environments because speech input allows the user to cut through layers of menus and to avoid using the tiny keypads found on mobile devices. On the other hand, graphical display of the information retrieved in the search has two advantages: first, it enables complex information to persist and second, it is also possible to provide maps and images as part of the search results that would be impossible to convey by voice alone. The application might also make use of the haptic modality by allowing the user to scroll through a list of results with a back and forth motion of the device. This hypothetical application would go through the following steps: 1.

2.

424

Startup a. User clicks a button on a web page to request a session b. The SCXML Interaction Manager (IM) on the server starts the application c. The IM sends an HTML page to web browser on the user’s device through a life cycle event (startRequest) d. The IM issues another startRequest which sends a VoiceXML page to a remote voice browser. This page defines the voice portion of the multimodal interaction Basic Search Operation a. The user speaks a search request “show me parking garages near here” b. The voice browser converts this request to EMMA and sends it back to the IM as part of a “done” life cycle event

c. d. e. f.

g.

h.

The IM submits the request to a search engine The IM receives the search results as an HTML page The IM sends the new HTML page with search results to the web browser The user moves the device back and forth to page through results. The user’s motions are interpreted by the haptic modality interpreter located on the device. An extensionNotification event is sent to the IM requesting a new result after each motion. The search ends when the user says “that one”, which sends an extensionNotification event to the IM indicating the user’s choice. The IM submits a request to a routing server, receives a page, and then sends the new HTML page with the route to the requested location to the user’s device.

FUTURE TRENDs Distributed Applications Yudkowsky (Yudkowsky, 2005) discusses the concept of “disaggregation”; that is, how breaking down systems previously considered to be monolithic into components creates market opportunities. Standards are a key aspect of disaggregation, because they promote the independent development of interoperable components. As an example, standards could support multilingual dialog applications like the one shown in Figure 10. Figure 10 shows a configuration of web services cooperating to provide distributed multilingual speech recognition, handwriting recognition, and natural language parsing services for French and English inputs, as well as language-independent dialog management and archiving. Because each service uses the EMMA language for representing


Figure 10. Distributed multimodal application

user inputs, the components could be developed independently, by language and modality experts, and hosted on different servers.

Future Modalities A key requirement for multimodal standards is to be able to accommodate future modalities. Because of the modality-independent nature of the MMI Architecture and EMMA, they are both very adaptable to future modalities. In the near future, haptic, or motion-based user inputs will become more common. Systems integrating ink and speech, for example, as described in (Adler & Davis, 2007), are being explored. Biometric inputs are also becoming important in general, and the variety of biometrics is also increasing rapidly. Simple brain-wave interpreters are also possible now and could potentially be integrated into future multimodal systems.

CONClUsION There exists a wide range of standards, both well-established and emerging, that are applicable to the development of multimodal applications. Together this set of standards provides a comprehensive suite of capabilities for enabling multimodal applications based on both current and future modalities. Table 2 summarizes the standards discussed in this chapter, along with the organizations that are responsible for them as well as their current standardization status. It is hoped that these standards will lay the foundation for many valuable current and future multimodal applications.

ACKNOWlEDGMENT I would like to thank the members of the W3C Multimodal Interaction Working Group and the W3C Voice Browser Working Group for many

425


Table 2. Summary of multimodal standards Standard

Organization

Status

MMI Architecture

W3C (Multimodal Interaction WG)

W3C Working Draft

EMMA


W3C Candidate Recommendation

InkML


W3C Last Call Working Draft

MRCPv2

IETF (SpeechSC WG)

Internet Draft

Biometrics

Various (ANSI, ISO)

Various

VoiceXML 2.0 and 2.1

W3C (Voice Browser WG)

W3C Recommendation

SRGS/SISR


W3C Recommendation

SSML


W3C Recommendation

PLS


W3C Recommendation

SCXML


W3C Working Draft

SVG

W3C (SVG WG)

W3C Recommendation

(X)HTML

W3C (HTML and XHTML WG’s)

W3C Recommendation

MMI Guidelines


W3C Note

Web Content Accessibility Guidelines 2.0 (WCAG 2.0)

W3C (Web Content Accessibility Guidelines WG)

W3C Candidate Recommendation

Mobile Web Best Practices

W3C (Mobile Web Best Practices WG)

W3C Recommendation

MobileOK tests

W3C (Mobile Web Best Practices WG)

W3C Last Call Working Draft

VoiceXML Platform Certification

VoiceXML Forum

Program is in place

VoiceXML Developer Certification

VoiceXML Forum

Program is in place

Architecture and Communication

Application Definition

User Interface

Certifications

interesting standards discussions over the past nine years which have lead to the development of many of the standards discussed in this chapter. I would also like to thank the World Wide Web Consortium for its support of this work.

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and standard communication protocols among system components

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Certification Standards: Standards which provide a formal definition of a term, such as “VoiceXML Platform” and certify that a system meets that definition.

Wayman, J., Jain, R., Maltoni, D., & Maio, D. (2005). Biometric systems: Technology, design, and performance. London: Springer-Verlag. West, D., Apted, T., & Quigley, A. (2004, September 7). A context inference and multimodal approach to mobile information access. Paper presented at the AIMS 2004: Artificial Intelligence in Mobile Systems, Nottingham, UK. Yudkowsky, M. (2005). The pebble and the avalanche. San Francisco: Berrett-Koehler Publishers, Inc.

KEy TERMs AND DEFINITIONs

Extensible MultiModal Annotation (EMMA): An XML-based language for representing user inputs and communicating them among system components. Semantic Interpretation for Speech Recognition (SISR): A language for associating speech grammar rule results with return values. Speech Recognition Grammar Specification (SRGS): A language for defining grammars for speech recognizers. State Chart XML (SCXML): An XML language for defining state machines. Speech Synthesis Markup Language (SSML): A standard markup language for speech synthesis

Application Definition Standards: Markupbased standards for defining application, for example, a standard format for writing speech recognition grammars.

User Interface Standards: Standards for the user interface, intended to help systems accommodate human physical, perceptual, and cognitive capabilities.

Architectural Standards: Standard ways of dividing functions among system components

VoiceXML: An application definition standard for voice dialogs.

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Chapter XXIII

Visualising Interactions on Mobile Multimodal Systems Kristine Deray University of Technology, Sydney, Australia Simeon Simoff University of Western Sydney, Australia

ABsTRACT The purpose of this chapter is to set design guidelines on visual representations of interactions for mobile multimodal systems. The chapter looks at the features of interaction as process and how these features are exposed in the data. It presents a three layer framework for designing visual representations for mobile multimodal systems and a method that implements it. The method is based on an operationalisation of the source-target mapping from the contemporary theory of metaphors. Resultant design guidelines are grouped into (i) a set of high-level design requirements for visual representations of interactions on mobile multimodal systems; and (ii) a set of specific design requirements for the visual elements and displays for representing interactions on mobile multimodal systems. The second set then is considered subject to an additional requirement – the preservation of the beauty of the representation across the relevant modalities. The chapter is focused on the modality of the output. Though the chapter considers interaction data from human to human interactions, presented framework and designed guidelines are applicable towards interaction in general.

INTRODUCTION Contemporary information and communications technology (ICT) offer an unprecedented degree of mobility, affecting the operating environments

of human endeavours. These environments may span multiple and changing contexts, be that offices, homes, outdoors, hospitals or any other environments where ICT is embedded in the processes that operate in such environments. The


Visualising Interactions on Mobile Multimodal Systems

concept of mobility of a device spans a range of platforms with different sizes, capabilities and interfaces. On the one end are familiar compact devices, such as mobile phones, personal digital assistants (PDAs) and integrated ICT devices, such as iPhone, when on the other end are emerging technologies of larger size, but still mobile and embedded in the environment, for instance, digital tables like Microsoft Surface platform1 which can be moved around in the physical environment in the same way as we arrange conventional furniture. Such systems, due to their spatial presence and relatively large interaction area, enable rich multimodal interaction (Tse et al., 2008), hence, require the development and support of efficient multimodal interfaces (Oviatt, 2003). Such interfaces require the development of new interaction paradigms in order to facilitate the design. Successful interface paradigms are recognised by their consistency and respective intuitive behaviour – a result of well-understood underlying metaphors and corresponding design patterns that can be articulated independently of any single application (Raman, 2003) Many application areas, that utilise mobile systems, require communication of information about the interaction process. The way interactions unfold can tell us a lot about both the process and the outcomes of the interactions. For example, in health care, the treatment process is expected to benefit if we are able to present the information about how patient-practitioner interactions unfold, and if, based on that representation, one can judge about whether there has been a good communication or not, and if not, where were the bottlenecks (Deray and Simoff, 2007). In design, the participatory design process may benefit if we can present the information about how clientdesigner interactions unfold and if, based on that representation, one can judge about whether design communication has been good or not, and align interaction patterns with the emergence of design solutions in order to revisit intermediate design solutions (Simoff and Maher, 2000). In interna-

tional negotiations, the negotiation process may benefit if we can present the information about how the interaction between negotiating parties unfolds, in order to monitor the flow of these interaction and provide indicators to a third-party trusted mediator to interfere before negotiations reach a deadlock (Simoff et al., 2008). These examples emphasise the interplay between information about interaction and informed decision making in respective application domains. Such interplay set the key goals in the pursuit of efficient and effective means for information communication in mobile multimodal systems, in particular: (i) enabling information fusion naturally into human communication and human activities; (ii) increasing the reliability of interactions within the information-rich environments; (iii) delivering information at the right level of granularity, depending on the context of the decision making process; (iv) enabling visual analytics based on communicated information through the different modality channels. In order to address these goals it is important to develop methods of encoding information about the way interactions unfold that both humans and mobile systems are able to process and utilise in order to improve interactions and respective processes. Further in this chapter we consider the specifics of interactions from the point of view of their visual representation in multimodal systems. The term “multimodal” has been broadly used in a number of disciplines. For the purpose of this chapter we adapt the definition of multimodal HCI system in (Jaimes and Sebe, 2007) to mobile systems. Multimodal mobile system is a mobile system that can respond to human inputs in more than one modality and can communicate its output to humans in more than one modality. In this chapter we take the stance that a design of visual representations of interactions in multimodal mobile systems that is based on the principles of natural (physical) systems offers seamless and consistent interface, allowing the human to concentrate on the task at hand. Though

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the framework of the underlying natural system may limit the range of visualisation elements and their operations when representing interactions, it ensures the consistency of the representations and the consistent integration of the visual representation in the existing interface. We then formulate a set of high-level design requirements for visual representations of interactions on mobile multimodal systems and a set of ten rules as specific design requirements for visual elements and displays for representing interactions on mobile multimodal systems. The chapter also briefly considers aspects of beauty that cut cross the design guidelines.

INTERACTIONs FROM A PERsPECTIVE OF VIsUAlIsATION ON MOBIlE MUlTIMODAl sysTEMs Interactions in real world scenarios are usually placed in the context of those scenarios. Following the examples in the previous section, in health care, patient-practitioner interaction is placed in the context of current patient condition, the medical history of the patient, previous interactions with the same practitioner and/or other practitioners, work conditions, family conditions and other possible factors that can influence the treatment process. In participatory design, the interaction between designers and the client is placed in the context of client’s requirements, designers’ experience, background knowledge of both parties, project budget, the outcomes of previous interactive sessions and other factors that can influence the participatory design process. In international negotiations, interaction between negotiators is placed in the context of current relations between negotiating parties, the regional and overall international arena, the history of the region, the outcomes of previous interactions and other factors that can influence the negotiation process. These examples illustrate the key features of interactions

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that need to be presented in multiple modalities. Interactions •

•

• • • •

Are inherently dynamic and depend on the information transfer between involved parties; Unfold in specific temporal spatial frames of reference that provide the context of enactment; Are embedded in collaborative and generative processes; Carry essential information about the process they are embedded into; Create channels that have certain characteristics in terms of information fusion; Operate through different modalities.

Figure 1 presents a framework for designing visual representations for mobile multimodal systems. The framework provides high level links of the key concepts involved in modelling interactions, expanded in the “Interaction Modelling Layer”, with the formation of the underlying representation constructs catering for multiple modalities (the “Representation Constructs Layer”) and the formation of respective visual language (the “Visualisation Layer”) – all these operating under the guidelines for designing visual representations and the guidelines for multimodal interface design. For the purpose of this chapter we adapt the approach towards modelling interactions presented in (Pinhanez, 1999). Within the framework actions are the building (unit) blocks of interactions. An action is a discrete unit of intentional behaviour that involves change in a given context. Actions are performed by individual parties. Then interaction is a series of actions between two parties that produce reciprocal effects as perceivable outcomes. In terms of communication, interaction is a two-way process, which involves an exchange of messages, the sequence of which forms a dialogue, not a semantically disconnected set of actions. Interactions unfold in time forming an interaction space - a collection of interactions


Figure 1. Framework for designing visual representations for mobile multimodal systems Guidelines for Designing Visual Representations

Visualisation Layer

Visual primitives and rules for: (i) operating with them; (ii) forming phrases; and (iii) interpreting these phrases that express the underlying constructs

Representation Constructs Layer Underlying representation constructs as the medium (vehicle) for expressing interactions in multimodal mobile systems

Guidelines for Multimodal Interface Design

Interaction Modelling Layer

Party 1

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An interaction

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Actions of Party 2 Interaction space Interaction system Interaction context

over a given period of time. Collection of interactions, bounded by a common task and/or time constraint, forms a session. Each session has an entry action, which is incorporated in the first interaction in the sequence and an exit action, which is incorporated in the last interaction in the session. An interaction space is bounded by the parties involved and can include one or more sessions. An interaction system encompasses the interaction space and the parties involved in it. Further the interaction system is embedded in the interaction context, which has components that are internal and components that are external to the interaction system. For instance, with respect to an individual interaction, the previous interactions within the interaction space between the same parties can be considered as a context. Interactions or relations of the parties, bounded by the interaction space, with other parties that are not part of the same interaction space are example of external components of the interaction context. This interaction model, which presents interactions as a physical system, provides the features of the interactions necessary for formulating the

requirements towards representation constructs from a computational perspective. From a physical system point of view interactions can be described through their: •

• •

•

•

Intensity, which indicates the strength of the structural coupling between the individual actions in a resultant interaction; Information fusion, which indicates the quality of communication; Decision path, which indicates the type of decision making that takes place during the interaction; Common ground, which indicates the amount of co-referencing, coordination and agreed event structure (see Coiera, 2001) for a formalised definition of common ground as the intersection of the internal models of two communicating parties; Dialogue features, which indicate the dynamics of the interaction sequences through a session.

Representational constructs that can express these features from computational perspective 433


require some form of quantification. (Acampora et al., 2006) provide an interesting approach considering symbolic representations for modelling human interactions within environments populated with sensors and actuators. (McCowan et al., 2003) provide an approach for looking at multimodal interaction and group actions, that is helpful when considering interaction at a higher level of granularity As a sequence of actions, interactions can be described through the various time-based statistics of actions. These include the length of each action, the average length and variance of actions of each party in an interaction, the frequency of turn taking and other similar parameters. Similarly, the interaction space can be described in similar parameters at the interaction level. These parameters describe interactions as a physical system and allow us to specify a guiding process of how to populate the Representation Constructs Layer in Figure 1. The design of visual representations, in broad sense, is the definition of the rules for conversion of data into graphics (graphical forms). Formal approaches utilise the concept of semantic visualisation, defined as a visualisation method, which establishes and preserves the semantic link between form of the visual elements and their function in the context of the visualisation metaphor (Simoff, 2008). The operationalisation of the framework in Figure 1 utilises the contemporary view of metaphors in cognitive linguistics (Lakoff and Johnson, 1980; Lakoff, 1993; Turner, 1994). These mechanisms of the metaphors are described in cognitive linguistics through the terms source and target, referring to the conceptual spaces connected by the metaphor. (In the research literature the target is variously referred to as the primary system or the topic, and the source is often called the secondary system or the vehicle). The target is the conceptual space that is being described, and the source is the space that is being used to describe the target (Lakoff, 1993; Turner, 1994). Correct mapping requires that the structure of the

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source domain is projected onto the target domain in a way that is consistent with inherent target domain structure (Lakoff, 1993; Turner, 1994). The model reflects the phenomena that humans usually use well known concepts from one (the source) domain when describing less (or even new) concepts in another (the target) domain. For the purpose of this chapter we consider conceptual spaces that describe physical systems, which usually include some formal description of such systems and a formal representation (language) for describing the systems. If both domains can be represented as physical systems then inherent features of the physical system in the source domain can be used to develop formal representation of the physical system in the target domain. The idea of the source-target mapping between the conceptual spaces that describe physical systems is illustrated in Figure 2 with an example of looking for concepts in human movement (the source domain) to be used to describe interactions (the target domain). These concepts then are formalised to construct representations for mobile multimodal systems. On the left-hand side is the source domain, which, in this example, describes human movement, on the right-hand side is the target domain that, in this example, will use constructs from human movement to describe interactions. The translational mapping is illustrated at three levels. The labels in the sections in the diagram for each level are indexed with “S” and “T”, indicating respectively “source” and “target”. The two physical systems are compared in terms of their features and dynamics in order to identify the underlying correlations and derive the concepts in one of the systems that are suitable to express the properties of the other. Diagram sections aS and aT in Figure 2 show the similarity between “human movement” from a physical system perspective. They show that both dancing and interactions can be structured as a sequence of states


S[t1 ], S[t2 ], , S[tn ] of the system in respective time intervals[t1 ], [t2 ],, [tn ]. Diagram section aT shows

that the states of the system S[t1 ], S[t2 ],, S[tn ] depend on the sequences of activities of the interacting parties

{P[ ], P[ ]},{P[ ], P[ ]},,{P[ ], P[ ]} 1

t1

2

t1

1

t2

2

t2

1

tn

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during the respective time intervals [t1 ], [t2 ],, [tn ] . In this example in the source domain the “Movement observation science” (Newlove and Balby, 2004) has been selected as the formal system for conceptualising human movement (diagram section bS). The physical constituents in the source domain, for example, effort and flow, and their expression are constructed through the behaviour of respective elements, in this example, through the behaviour of shaping affinities of elasticities and qualities. The temporal expressions of the concepts from the source domain, the effort and flow in this case, can be interpreted as shape deformations. These expressive properties of the physical systems suitable for the source domain are the enablers of the translation into the target domain, where the morphodynamics of interactions now can be expressed through the changing geometry of the models of elasticities and qualities (for details see (Deray and Simoff, 2007)). Morphodynamics here is used in a non-geological sense, to label the evolution of the interaction landscape (i.e. treating interaction as a single entity) as a result of the contributions of participating parties. This derivation is schematically illustrated in diagram section bT, Figure 2. The upper part shows visualisation elements that reflect the underlying computational models of elasticities and qualities, catering for literal interpretation of the term “geometry of interactions”. The lower part shows an audio reflection of the underlying computational models of elasticities and qualities, enabling another mode for representing interactions in a multimodal system. The introduction of

multiple modalities is enabled by the introduction of the common computational representation that then maps on to specific visual, audio or other modal representation. Diagram sections cS and cT in Figure 2 show that in some cases the analogical mapping between the systems could extend even to the level of the formalisms used – in our example scores are used both in the source and target domain. The score in the source domain in diagram section cS is a fragment of Labanotation. It is mainly used at theatres to archive most commonly ballet,2 however, there has been an increasing interest in it from the field of human-computer interaction spanning from motion segmentation (Bouchard and Badler, 2007) to enabling affective modality (Fagerberg et al., 2003). The scores in the target domain in diagram section cT are following the two modalities of presentation: visual, generated according to the visual language described in (Deray and Simoff, 2007) and adapted for mobile devices in (Deray and Simoff, 2008) and audio, which, only for illustrative purposes is shown as a visualisation with the mapping used by WolframTones3. The sheet music scores and Tibetan musical scores illustrate the idea of having multiple possible formal representations, depending on the specific implementation of the modality input. In other words, the different scores are generated from data about interactions kept and updated at the Representation Constructs Layer in the framework in Figure 1.

Design Guidelines for Visualisation of Interactions on Mobile Multimodal systems We synthesise the design guidelines in the context of our framework in Figure 1. The guidelines are a synthesis, which also incorporates elements from: •

The principles for designing visual languages for representing interactions, formulated

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Figure 2. Operationalisation of the framework in Figure 1 Physical system (Source domain): Human movement

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as

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in (Deray and Simoff, 2007) and extended to mobile systems in (Deray and Simoff, 2008); The guidelines for multimodal user interface design, presented in (Reeves et al., 2004) and the user interface principles for multimodal interaction in (Raman, 2003); The discussions of user and task modeling, multimodal fusion, taxonomy of techniques, as well as the broad consideration of the body, gesture, gaze, and affective interaction in (Jaimes and Sebe, 2007);

High-Level Design Requirements for Visual Representations of Interactions on Mobile Multimodal systems This section addresses the high-level design guidelines. (Reeves et al., 2004) suggest that the interface design for multimodal systems should cater for broadest range of users and contexts of use. Consequently, (Reeves et al., 2004) suggest that designers should become familiar with users’ psychological characteristics. Further we discuss

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specific requirements along these lines and the guidelines that address them. Consistency relates to common features in the design that can accommodate different tasks by using dimensions that reference similar phenomenal experience. Choosing these dimensions provides consistency of the geometrical or topological structures (Gärdenfors, 2000; Gärdenfors, 2005). In turn this partially depends on what modalities are assumed to be used and their function. Within the framework proposed in this chapter this requirement is addressed through the selection of the source domain. For instance, the design of spring-based visualisations of graphs uses the physics of spring model as the source domain to specify the visual representation components, the relations and behaviours in the visualisation (target) domain. In Figure 2 we have used the human movement domain as a source for describing interactions. The fundamental premise behind such choice is the research in embodied cognition according to which intelligence has a bodily basis of thought (Pheifer and Bongard, 2007). (Deray and Simoff, 2007; Deray and Simoff, 2008) demonstrated the feasibility


of such approach for developing mechanisms for visualisation of interactions in general, including mobile systems. As requirements evolve in time, the source-target approach offers also a set of constraints in terms of consistency against which the design solutions that address such evolving requirements are tested. Adaptivity can only be consistently supported if users can identify the logic for the adaptation, to say, a different interaction context (e.g. visualising healthcare interactions during a patient visit and during a lecture to medical students) or a different user (e.g. visualising interactions to a patient or to a doctor). Having a common formal representation (see Figure 2) enables adaptivity. The above two requirements for consistency and adaptivity lead to the requirement for synchronising multiple modalities. Though this chapter is limited to the discussion of output modalities, it’s worth considering (Raman, 2003) points about the temporal and spatial dimensions of interaction, which, in some sense, reflect its sequential and parallel (multimodal) nature, respectively. In some we can speculate about a “second order” multiple modality, i.e. we have to be able to represent in our multimodal outputs data about multimodal interactions themselves. For instance, a phone interview is primarily characterised by the sequence of speech utterances over the phone – the sequence in turn-taking and the content of the individual speech acts. An interaction in a face-to-face interview may include also passing information on a shared document through pointing, gestures over specific parts of the document and other activities part of interaction. Extending this to the interactions during the deliberations of the selection committee leads to parallel streams of utterances and spatial elements (e.g. committee member can point to a particular paragraph and figure in the document that another member is looking at) within the same time interval. In terms of design requirements, in order to enable synchronisation between multiple modalities in the output, representation of interactions should

be capable of capturing both the temporal and spatial dimensions, as well as parallel communications. Within the framework of this chapter this is achieved with the design of a common formal representation, from which then are generated respective modality outputs. In essence each modality should interact with a common underlying representation of interactions. Having common underlying representation will enable also smooth transition between presentation modalities. This element of the guidelines not only reflects the natural change in the modalities of human interaction, but also the change in the environment in which a multimodal mobile system operates. For example, designers will need to cater for changes not only in the bandwidth for communication between the mobile device and the user (e.g. screen size, audio level in comparison to surrounding noise), but also for the bandwidth of communication between the mobile device and the network, when switching between different networks. In other words, in a collaborative session the selection of a high resolution visualisation of the interactions may be inappropriate if it is shared between two devices that are on heavily loaded network. In some sense, this extends the adaptability requirement in terms of adapting to users environment and to the operational environment. Figure 3 illustrates aspects of compliance with the above discussed guidelines on an example from visualising interactions in healthcare. The underlying representation of interactions uses a score type of formal representation, allowing the generation of visual and audio output modalities. Within the visual modality, depending on the context in which the user operates the system can shift to different displays combining text, table, graphics and video display. In a scenario, when specialists provide remote consultation, the choice of the combination of displays between Figure 3a – Figure 3c may depend on the current task, as well as the network connection.

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Further we present a set of specific requirements for the construction of the visual elements and combinations of such elements, which constitute the following section.

•

Specific Design Requirements for Visual Elements and Displays for Representing Interactions on Mobile Multimodal Systems The guidelines for the design of visual elements and combinations of them are necessary for the development of specific visual primitives and their behaviour, so that they can support the tasks considered in the system design specifications. These guidelines are synthesised including requirements specified in (Deray and Simoff, 2007) (based on adaptation of requirements enlisted in (Pretorius, 2005) and requirements extension in (Deray and Simoff, 2008). The specific design requirements are formulated as ten rules. The aspects of beauty in human-computer interactions that should be taken in consideration in the design are then discussed across these ten rules:

•

•

Rule 01–Symmetry: The argument for the symmetry comes from our view at the interaction as single object whose parameters are defined by the combined activities of contributing parties. Symmetry is expected to allow users to identify “balanced” interactions on each visual axes and to discover interaction patterns typical for particular scenarios and situations. The description of such set of patterns is a requirement in these guidelines. Rule 02 – Regularity: The composition rules that are used to integrate visual elements should produce visual phrases with regular (normalised) structures. Such format of the visual phrase enables the comparison of visual elements within and between visual phrases. Rule 03– Similarity: The visual elements should have similar appearances in terms of colour and shape. The size should be normalised and its value should be generated based on the output from the common representational layer (see Figure 1).

Figure 3. Examples of a single visual modality with different combinations of displays

a. Text, graphics and video displays

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c. Table data and graphics displays


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Rule 04–Connectivity: The visualisation elements should be conceptually connected, ideally forming a visual language as mobile multimodal systems are expected to support analysis of interaction in variety of situations and contexts. Rule 05–Legend: In general, the visual representation should be equipped with algorithms for attaching semantic labels to the visual components when required by the task or the user. Rule 06–Containment and Aggregation: The visual representation should incorporate rules for aggregating visualisation elements for representing interactions at different levels of granularity. Membership to an element to an upper level is represented by containment. For example, in the visual language for representing interactions in (Deray and Simoff, 2007) the production range element is visually similar to the production element for achieving cognitive consistency. When catering views of large amounts of interaction data, aggregations also reduce the visual cluttering of the display. Rule 07 –Transparency: This design principle is used for enabling comparison of visual elements and respective analysis by aligned superimposing of the elements. This feature is useful when analysing discrete snapshots of collections of interactions over specific time intervals or interactions with similar structures of participating parties obtained over the same time interval. Transparency can also be used to emphasise the elements in focus, or to reduce the visual cluttering when displaying many scores. Rule 08–Complexity: This design principle aims at achieving low representational complexity, so that through the visual representation the development and unfolding of interactions can be monitored in real-time on a mobile device. Rule 09–Expandability: This design principle enables the development of a visuali-

•

sation that can accommodate change in the representation of interaction information. In other words, visual elements should be designed in a way to accommodate gradual expansion. This principle is key to the gradual incorporation of data from multiple interaction modalities in the visual representation. Rule 10–Scalability: This design principle addresses not only the size of the interaction data displayed, but the diversity of screens in mobile multimodal systems on which visualisation can appear, so that the displayed components remain recognisable under projective transformations, such as scaling, rotation, translation or if presented in perspective.

The underlying representation of interactions should preserve beauty across different modalities. This means that a visual aesthetics should be preserved if the information is presented in audio form. In other words the consistency of aesthetics in representations needs to be carried over different modalities. This principle is separate from the ten rules as it cuts across them. Rule 01 Symmetry and Rule 02 Regularity are enabling features of a visual design that complies with the beauty requirement. In the rest of the rules the compliance with the beauty comes as a constraint on the forms, proportions, ratios and other features in the representation. The six articles in the special volume 19 of the Journal of Human-Computer Interaction (HCI) for 2004, devoted to the different aspects and perspectives of beauty in HCI, is a must reading to be considered when thinking through the design of the visual representations of interactions.

CONClUsION The importance of interactions as a source of information has been recognised in many areas of human endeavour. This recognition includes not 439


only the content of the interactions (for instance, the numerous transcripts of conversations) but also the information about how the interactions unfold. The later information has been given less attention in terms of the development of technologies that capture such information. In the chapter we illustrated these aspects with examples from the fields of health care, design and negotiation. The advent of mobile multimodal systems offers platforms for capturing and presenting this information. The chapter has focussed specifically on the formulation of a framework for designing visual representations of information about the interaction process in a way that such information can be used in the decision making process in respective fields. The chapter analysed the process of unfolding interactions and the features of that process from a visualisation perspective. We have presented a model of the interaction space and three layer framework for designing visual representations for mobile multimodal systems. Further we discussed a method that enables the implementation of the framework. The method is based on the work in the contemporary theory of metaphors and the source-target operational model (Lakoff, 1993; Turner, 1994). The design guidelines are focused on the modality of the output. They have been presented in two sets: (i) a set of high-level design requirements for visual representations of interactions on mobile multimodal systems; and (ii) a set of ten specific design requirements for the visual elements and displays for representing interactions on mobile multimodal systems. We discuss the preservation of beauty across the visualisation elements as a constraint across the design requirements. When making decisions it is worth recalling the 10 myths of multimodal interaction (Oviatt, 1999). In particular, when it comes to design decisions on supporting multiple modalities for representing interactions it is worth taking in consideration the following myths:

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• • •

Myth #1 (“If you build a multimodal system, users will interact multimodally”); Myth #6 (“Multimodal integration involves redundancy of content between modes”) Myth #10 (“Enhanced efficiency is the main advantage of multimodal systems”)

The myths are cited as formulated in (Oviatt, 1999) – keep in mind that each myth is formulated as the opposite of the actual point that is made.

ACKNOWlEDGMENT This research is supported by University of Technology, Sydney and University of Western Sydney, Australia.

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Deray, K., & Simoff, S.J. (2008, October 20-23). Visualising the dynamics of unfolding interactions on mobile devices. Advances in Conceptual Modeling-Challenges and Opportunities, Proceedings of the ER 2008 International Workshop on Modeling Mobile Applications and Services (M2AS’08 ), Barcelona, Spain (pp. 238-247). Springer. Fagerberg, P., Ståhl, A., & Höök, K. (2003). Designing gestures for affective input: An analysis of shape, effort, and valence. In Proceedings of the International Conference Mobile Ubiquitous and Multimedia MUM 2003, Norrköping, Sweden (pp. 57-65). ACM Press. Gärdenfors, P. (2000). Conceptal spaces. Cambridge, MA: MIT Press. Gärdenfors, P. (2005). The dynamics of thought. Heidelberg: Springer. Jaimes, A., & Sebe, N. (2007). Multimodal humancomputer interaction: A survey. Computer Vision and Image Understanding, 108, 116-134. Lakoff, G. (1993). The contemorary theory of metaphor. In A. Ortony (Ed.), Metaphor and thought (pp. 202-251). Cambridge, MA: Cambridge University Press. Lakoff, G., & Johnson, M. (1980). Metaphors we live by. Chicago, IL: University of Chicago Press. McCowan, I., Bengio, S., Gatica-Perez, D., Lathoud, G., Monay, F., Moore, D., Wellner, P., & Bourlard, H. (2003). Modeling human interaction in meetings. In Proceedings of ICASSP 2003, IEEE Press: IV-748 - IV-751. Newlove, J., & Balby, J. (2004). Laban for all. London: Nick Hern. Oviatt, S. (1999). Ten myths of multimodal interaction. Communications of the ACM, 42(11), 74-81.

Oviatt, S.L. (2003). Multimodal interfaces. In J. Jacko & A. Sears (Ed.), The human-computer interaction handbook: Fundamentals, evolving technologies, and emerging applications (pp. 286-304). Mahwah, NJ: Lawrence Erlbaum. Pheifer, R., & Bongard, J. (2007). How the body shapes the way we think. Cambridge, MA: MIT Press. Pinhanez, C.S. (1999). Representation and recognition of action in interactive spaces. Unpublished doctoral dissertation, Cambridge, MA, Massachusetts Institute of Technology. Pretorius, A.J. (2005). Visual analysis for ontology engineering. Journal of Visual Languages and Computing, 16, 359-381. Raman, T.V. (2003). User interface principles for multimodal interaction. Workshop on Principles for Multimodal User Interface Design, CHI 2003. Retrieved from http://www.almaden.ibm.com/cs/ people/tvraman/chi-2003/mmi-position.html Reeves, L.M., Lai, J., Larson, J.A., Oviatt, S., Balaji, T.S., Buisine, S., Collings, P., Cohen, P., Kraal, B., Martin, J.-C., McTear, M., Raman, T., Stanney, K.M., Su, H., & Wang, Q.Y. (2004). Guidelines for multimodal user interface design. Communications of the ACM, 47(1), 57-59. Simoff, S.J. (2008). Form-Semantics-Function-a framework for designing visual data representations for visual data mining. In S.J. Simoff, M.H. Böhlen & A. Mazeika (Eds.), Visual data mining: Theory, techniques, and tools for visual analytics (LNCS 4404, pp. 30-45). Heidelberg: Springer Verlag. Simoff, S.J., & Maher, M.L. (2000). Analysing participation in collaborative design environments. Design studies, 21, 119-144. Simoff, S.J., Sierra, C., & López de Mántaras, R. (2008, September 17). Requirements towards automated mediation agents. In Pre-proceedings of the

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KR2008-Workshop on Knowledge Representation for Agents and MultiAgent Systems (KRAMAS), Sydney, Australia (pp. 171-185).

Metaphor in this work is the system and process of understanding and representing one idea, or conceptual domain, in terms of another

Tse, E., Greenberg, S., Shen, C., Forlines, C., & Kodama, R. (2008, February 25-27). Exploring true multiuser multimodal interaction over a digital table. In Proceedings of DIS 2008, Cape Town, South Africa (pp. 109-118).

Multimodal Mobile System is a mobile system that can respond to human inputs in more than one modality and can communicate its output to humans in more than one modality.

Turner, M. (1994). Design for a theory of meaning. In W. Overton & D. Palermo (Eds.), The nature and ontogenesis of meaning (pp. 91-107). Lawrence Erlbaum Associates.

KEy TERMs AND DEFINITIONs Common Ground is an aggregated measure of the intersection of the internal models of two communicating parties and includes co-referencing, coordination and agreed event structure. Conceptual space in this work is a space built up on key quality dimensions that describe and structure the physical system needed for representing concepts.

Physical System is a system that operates on natural principles and is the portion of the physical material world chosen for analysis. Semantic Visualisation is a method that establishes and preserves the semantic link between form of the visual elements and their function in the context of the visualisation metaphor. Translational Mapping is the term applied collectively to the levels of mapping from the source to the target domain

ENDNOTEs 1

2

Dialogue Features indicate the dynamics of the interaction sequences through a session. Interaction is a series of actions between two parties that produce reciprocal effects as perceivable outcome.

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http://www.microsoft.com/surface/index. html See “Introduction to Labanotation” [http:// user.uni-frankfurt.de/~griesbec/labane. html] for a quick introduction of how the notation looks like and how the notation analyses movement. http://tones.wolfram.com/

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Chapter XXIV

Usability Evaluation of Multimodal Interfaces Regina Bernhaupt IRIT, IHCS, Toulouse, France

ABsTRACT In order to develop easy-to-use multimodal interfaces for mobile applications, effective usability evaluation methods (UEMs) are an essential component of the development process. Over the past decades various usability evaluation methods have been developed and implemented to improve and assure easyto-use user interfaces and systems. However, most of the so-called ‘classical’ methods exhibit shortcomings when used in the field of mobile applications, especially when addressing multimodal interaction (MMI). Hence, several ‘classical’ methods were broadened, varied, and changed to meet the demands of testing usability for multimodal interfaces and mobile applications. This chapter presents a selection of these ‘classical’ methods, and introduces some newly developed methods for testing usability in the area of multimodal interfaces. The chapter concludes with a summary on currently available methods for usability evaluation of multimodal interfaces for mobile devices.

INTRODUCTION Designing and developing usable interfaces and interaction techniques is one of the main goals in the area of human-computer interaction. The goal of easy-to-use interfaces is achieved by applying iterative and user-centered development. During the various stages of the product development several forms of evaluation take place,

aiming to inform the product design to increase the usability (and today also the user experience) of the product. The classical forms of usability evaluation methods were developed starting in the early 1980ies. Main goal was to increase the usability of desktop applications. These applications were typically used by a more or less clearly described user group, used in a fixed (working) environment


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with the goal to support the productivity of the user by achieving goals fast, efficient and easy. Today the rapid development of mobile services and devices asks for new forms of usability evaluation: mobile applications are used by “almost” everyone, in all kinds of places—public or private, for business, leisure or entertainment, to achieve clear goals or to simply be entertained during a waiting period, together or with a group of people—in the same location or even shared remotely. This multitude of usage scenarios explains why mobile devices today try to use multimodal interaction techniques. Multimodal interaction techniques and interfaces (MMI) are seen as a good way to increase customer satisfaction, user experience and comfort by providing a more natural interaction (Boldt, 1992). Besides that, multimodal interaction can increase the bandwidth between the user and the system. Modalities are often complementary, thus multimodal interfaces can help to clarify and reinforce the communication between the user and the system (Oviatt, 1999). Mobile devices and services on the other side suffer sometimes from limited communication bandwidth like small screens or non-availability of keyboards, and communication between user and system is extremely influenced by contextual factors, like usage location, usage alone or in groups, usage time or users pre-knowledge. Based on the benefits multimodal interaction is promising, it has become common to use multimodal interfaces in mobile applications to extend the sometimes limited communication bandwidth, for example Pirhonen, Brewster and Holguin (2002) using gesture and non-speech iconography to improve the user interface. To develop easy-to-use multimodal interfaces for mobile applications continuous usability evaluation within the (most often) user-centered and iterative development process is a necessity. When evaluating multimodal interaction with mobile

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devices the specifities of mobile usability evaluation and multimodal usability evaluation have to be taken into account. The evaluation must take into account that usability problems might be influenced by the current location of the user, a multimodal interface including sound (e.g. earcons) as feedback might not be appropriate if the current environment is noisy. Thus new forms of evaluation are needed to investigate the real usage of the system. In-situ methods can help to investigate the real usage of the system in the field. Using prototypes in the field might help to discover new forms of usability problems – on the other side a fully functional prototype must be available to use this kind of usability evaluation methods. This chapter will describe these specifities of evaluating multimodal interaction techniques and interfaces for mobile applications and devices and how the possible methodological shortcomings can be coped with by using a mix of methods and approaches. The next section will give an overview on usability evaluation methods used for mobile applications, next an introduction on usability evaluation for multimodal interfaces is given. A short case-study shows how to apply a methodological mix of usability evaluation methods for the development of a mobile tourist guide including multi-modal interaction. Finally the chapter is concludes summarizing the most important decision criteria for choosing the right kind of evaluation method.

UsABIlITy EVAlUATION OF MOBIlE APPlICATIONs During the evaluation of mobile applications and services it is typical to focus on providing solutions for technical problems. In-depth studies on what kind of classical methodologies for usability evaluation can be used and how these classical methods have to be adopted for usage in mobile


scenarios are rare. But it is commonly accepted that data collection for evaluation of mobile devices and applications is a central challenge, and that novel methods must be found for that (Isomursu, Kuutti & Värinämo, 2004). Jones and Marsden (2006) provide in their introductory book on mobile interaction design an overview of currently applied evaluation methods. For early stages within the development cycle, they name a form of ethnography (“Quick and Dirty”), conceptual model extraction, direct observation (often called usability studies or usability testing), interviews, questionnaires, and experiments. Other methods listed that are not involving the user are heuristic evaluation and theoretical models. Looking at the real distribution of evaluation forms in scientific literature Kjeldskov and Graham (2003) conducted one of the most comprehensive reviews on evaluation methods used in HCI. They selected papers focusing on mobile HCI from relevant conferences over the past years and classified them according to the described research methods. The findings show that the prevalent mobile HCI research falls into the applied category (55%) followed by laboratory experiments (31%). On the bottom portion of the scale, only three and four (out of 102) research methods were conducted as case and field studies respectively. These findings imply that there is a strong tendency towards environment independent and artificial settings in HCI research. This lack of real-use-contexts and natural setting research could be justified by the fact that mobile HCI has strong roots in the field of computer sciences and HCI. These fields have a strong bias towards engineering and evaluation methodology. In addition, the methods stemming from the social sciences are normally more costly and resource intensive. Bernhaupt, Mihalic and Obrist (2008) summarize usability evaluation methods for mobile application and services in four classes:

•

•

•

•

User testing (Thinking aloud protocol, Wizard of Oz, Log file analysis, field observation, laboratory observation, …); Inspection oriented methods (like heuristic evaluation, cognitive walkthrough or guideline review); Self-reporting and inquiry oriented methods (like diaries and interviews, cultural probes, user experience sampling); Analytical modeling (task model analysis and performance models).

This classification is based on traditional classifications for UEMs and our experience in the field of evaluating mobile applications and services. More general classifications for methods in the field of mobile HCI are available focusing on the way data is collected (Hagen, Robertson, Kon & Sadler, 2005). In the field of mobile applications most of the above mentioned methods are used to some extent, with analytical modeling being used only in some specialized areas (saftety-critial applications). User Testing or usability studies typically are conducted in the laboratory, involving a user, who is performing a set of typical tasks. The users are asked to think aloud while performing the tasks. The system tested can be a real functional system, or the system is just acted out or a Wizard-of-Oz setting is used to pretend the multimodal capabilities of the system (Salber & Coutaz, 1993). The performance of the users (in terms of problem solution, clicks/key-presses, chosen combination of modalities) is most often logged or observed. Observation can take place by some experimenters/researchers or by using cameras in the test-setting. Evaluating the usability of mobile applications a usability study in a lab might not be sufficient. To investigate the real usage context, testing in the field or in “real situations” is necessary to learn about possible usage problems. Inspection-oriented methods on the other side involve an expert. The experts are using existing

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guidelines and they try to judge possible usability problems based on their experience. Expert methods are most often referred to as easy, fast and cheap, but they require a trained expert (Nielsen & Mack, 1994). The results heavily depend on the expert’s knowledge and experience. If you want to learn more about how to conduct a usability evaluation for mobile devices, the introductory text book of Jones & Marsden (2006) is helpful. The group of self-reporting and inquiry oriented methods is an emerging set of usability evaluation methods that is useful for investigating usage of mobile devices and services. Users typically interact with their mobile devices in daily life, and at the same time report their experiences, problems and insights to help inform the product development process. The last group of analytical models is used especially to evaluate possible interaction techniques for mobile devices. Based on some general laws (e.g. Fitts law) the time, duration, or mean-error rate can be computed. This helps to get a better understanding of the implications the selection of an interaction technique might have for daily usage. When selecting an appropriate evaluation method for mobile devices and systems we have to take into account the following specifities (Jones & Marsden, 2006): •

•

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End-users or experts: Who is going to conduct the evaluation, will it be a “real” user or an expert based evaluation? Context: Where will the evaluation take place? We have to realize that what is useful and what is perceived as problematic from a user perspective is influenced by the real usage conditions and the development of the usage over time. Studies in the lab started to simulate the context of usage and expertoriented evaluation methodologies have been varied, to enable the inclusion of the usage context.

•

Results: The various forms of evaluation will inform the design process in different ways depending on the stage of the development (low vs. high fidelity prototypes).

Evaluation of usability in mobile settings is thus influenced by the method selected, and results can only be useful if the appropriate method is chosen in the respective development stage. A good starting point for the discussion on what kind of data can be gathered with user testing during which step of development process is the SEMCPU approach (Lee, Hong & Smith-Jackson, 2005). The authors are proposing a systematic evaluation method for cell phone user interfaces, showing clearly the different kind of data that can be gathered during an experimental usability study for each step of the development process.

UsABIlITy EVAlUATION METhODs FOR MUlTIMODAl INTERACTION Evaluating the usability of a multimodal system can be conducted with any of the above listed methods. In the following we briefly describe each category and some specific methods used to evaluate multimodal interaction. A more detailed description of usability evaluation for MMI can be found in Bernhaupt, Navarre, Palanque and Winckler (2008). To investigate usability and user experience of multimodal interfaces, in general user testing is applied. The observation and recording of user’s activity helps to understand how different modalities can be used beneficial by the user and what kind of possibilities and restrictions arise from the combination of various input and output modalities. Several types of user testing have been conducted both in usability laboratories and in field studies. Studies in the lab or in the field are neither good nor bad, but must be selected based on research question, focus of investigation and intended results usability.


Evaluation based on inspection methods are conducted by a usability expert and rely on the expert’s knowledge on usability or existing guidelines with recommendations. This category includes Cognitive Walkthrough (Lewis, Polson & Wharton, 1990), formative evaluation and heuristic evaluation (Nielsen & Mack, 1994) and also benchmarking approaches covering issues such as ISO 9241 usability recommendations or conformance to guidelines (Bach & Scapin, 2003). Expert based evaluation methods are rarely used in the area of multimodal interfaces, as there seems to be a lack of ergonomic knowledge. Knowledge is not only missing in terms of experts experience for the design of multimodal systems but also due to the lack of guidelines to cover all potential modalities and modality combinations that might be encountered in multimodal interfaces. The proposed methods, especially cognitive walkthroughs, are designed to assess the achievement of goals focusing on the goal-structure of the interface rather than on interaction techniques. Questionnaires have been extensively employed to obtain qualitative feedback from users (e.g. satisfaction, perceived utility of the system, user preferences for modality). When investigating cognitive workload of MMI the NASA-TLX method is helpful (Brewster, Wright & Edwards, 1994), and was already adopted for MMI in mobile settings (Prihonen, Brewster & Holguin, 2002). More recently, simulation and model-based checking of system specifications have been used to predict usability problems such as unreachable states of the systems or conflict detection of events required for fusion. Bernhaupt, Navarre, Palanque and Winckler (2008) proposed to use formal specification techniques in combination with traditional usability evaluation methods like user testing or cognitive walkthroughs to support the evaluation of multimodal interaction techniques, to enable a detailed verification and analysis of usability problems, related to the fission and fusion of modalities.

EVAlUATING MUlTIMODAl INTERFACEs IN MOBIlE sETTINGs For the evaluation of multimodal interaction techniques (MM) for mobile devices we have to take into account the shortcomings and difficulties from the two respective areas. Related to the mobile nature, we have to carefully investigate the possible usage contexts that might affect the usability of the product (Taminnen, Oulasvirta, Toiskallio & Kankainen, 2003). The physical context is typically described as the location where the user is currently interacting, the technological context is the context of technology currently available to the user (e.g. the network, additional input or output devices like keyboards or screens), and the social context, representing all kind of situations influenced by social connections (like being upset that someone does not call). Please note, that the usability of a system is reasonable reflected, when you consider the above three contexts (Jones & Marsden, 2006). Other models of context typically refer to other settings like the home and are helpful to understand the adoption and long-term usage of a product (e.g. Venkatesh, 2006). Following you find a list of evaluation methods that can be helpful for investigating MMI for mobile devices. Each method is briefly explained and their benefits and shortcomings for evaluation of mobile MMI are given.

User Testing of Mobile MMI Usability tests, user studies and experiments conducted in the lab are performance measurements to determine whether usability goals have been achieved. The usability evaluation of mobile devices with MMI in the lab has several advantages: the conditions for conducting the test can be controlled; all participants experience the same setting leading to higher quality data. On the other hand, user testing in the field allows discovery of usability problems related to the context of use.

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Pascoe, Ryan and Morse (2000) studied the effects of using mobile devices while on the move, especially HCI related issues involved in using mobile devices in a real world (field) environment. The main factors which influence the interaction between users and mobile device include contextual awareness, task hierarchy, visual attention, hand manipulation, and mobility. These are critical issues of usability and mobile application design. Testing in a real environment means that test users are able to behave as they would normally, to the extent that they need not to be told to do so (Bernhaupt, Mihalic & Obrist, 2008). Especially when investigating MMI for mobile services, studies conducted in the field might help to discover new forms of usage problems. Sound output might not be possible in noisy environments, or are socially not accepted. The current situation (like standing in a bus for local transportation) might not allow usage of all types of modalities. Field studies help to discover usability problems that are not discovered with other methods. Whether usability studies should be conducted in the lab or in the field is still a matter of discussion and needs further research. How to conduct usability testing in “real-world” situations is also addressed in other areas of HCI. For example Bennett, Lindgaard, Tsuji, Conelly and Siek (2006) conducted a workshop on how testing in non-traditional environments can be conducted. Experts from domains like military, healthcare or mobile devices discussed on how methods have to be adopted to address testing in non-traditional environments. The general agreement was that only a mix of several methods during design and evaluation can help to ensure usability of the product. To summarize, user testing is helpful to gather insights on the real usage of the product and related usability problems. Especially when looking at multi-modal interaction, the usage context is a typical source of usability problems, as certain modalities might not be used in vari-

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ous usage situations. A user testing should thus take into account the usage context, by either simulating the real usage situation or testing in the field. To overcome limitations of user testing a combination with inspection oriented methods and self-reporting methods during early design phases is helpful.

Inspection Oriented Methods Industry typically relies on expert or inspection oriented usability evaluation methods, as they are said to be fast and cheap. A prominent inspection oriented method is the so called heuristic evaluation (see Nielsen & Mack, 1994 for a description on how to conduct the method and its benefits and shortcomings). Other methods focus on special aspects of usability, for example the cognitive walkthrough focuses on the learn-ability of the system (Nielsen & Mack, 1994). When applied to evaluating mobile (multimodal) systems, inspection-oriented methods lack validity. These methods do not take into account the contextual factors that affect user-system interaction (Johnson, 1998; Kjeldskov & Graham, 2003). Inspection oriented methods thus depend on the expert’s ability to interpret the context of use, the difficulties of the usage of the various modalities and their interdependencies. To overcome the shortcomings of the interpreting the usage context, Gabrielli, Mirabella, Kimani and Catarsi (2005) enhanced the cognitive walkthrough method including videos describing the usage context or by simply performing the cognitive walkthrough in a typical environment. They found that providing evaluators with a video recorded usage of the system in a real environment or doing the evaluation in a real context increases the number of problems detected by 63 % and 68 % respectively, compared to conducting a normal heuristic evaluation. However, they also found that the number of problems discovered in the video enhanced situation compared to the real context situation, did not differ. Nevertheless, us-


age of video or performing the evaluation method in a real setting helps to discover context-related usability problems. Po, Howard, Vetere and Skov (2004) enhanced Nielsen’s heuristic evaluation to include usage scenarios and varied the extended methodology to be conducted either in the lab or in the field. The enhanced heuristic evaluation helps to find usability problems which are related to timing sequences and that usability problems found in the extended methodology condition are typically more severe than problems found with the “normal” heuristic evaluation. The modification of inspection oriented methods helps evaluators to combine the advantages of these methods and reduce the shortcomings of the methods when applied in the field of mobile systems and applications. On the other hand, these methods rely on the knowledge and expertise of the experts. As detailed ergonomics on multi-modal interaction with all their possible combinations are missing, usage of inspection-oriented methods for evaluation is limited in the area of multimodal interaction for mobile systems, and most HCI oriented research is still conducted using user studies (Jones, Buchanan, Thimbelby, 2002; Mizobuchi, Mai Ren & Michaki, 2002).

Self Reporting and Inquiry To overcome the shortcomings of several usability evaluations methods not being able to take into account the usage context, so called in-situ, self reporting or inquiry-based evaluation methods have been developed and adopted. In-situ methods describe all kinds of methods that can be used to evaluate a system or prototype in its real usage context. During field usability studies or trials, questionnaires and self-reporting methods are used to evaluate possible usage difficulties or hindering effects. In-situ methods typically include ethnographic studies, (video) observation, contextual inquiries, or interviews. All these methods have in common, that they

try to explore usage of the (multimodal mobile) system in the real usage context, by investigating possible usage problems. One of the most prominent self-reporting approaches (Sanders, 2002) are the various forms of probing – cultural probing (Gaver, Dunne & Pacenti, 1999), technology probing (Hutchinson et al., 2003) or playful probing and creative cultural probing (Bernhaupt et al., 2007). Cultural probes were developed by Gaver et al. (1999) and can be used to investigate early design phases, mainly gathering insights on the users’ context in order to better inform the design process in an early stage (Gaver et al., 1999; Jääskö & Mattelmäki, 2003). To investigate usage of a (functional) prototype or system, technology probes were invented. Technology probes involve the deployment and evaluation of a new technology or application into a real use context (Hutchinson et al., 2003). The main advantage of this approach for usability evaluation purposes is providing real life insights on a number of usability issues (e.g. how users choose to interact with a certain device in a special context), which have to be considered in the user-centered design and development process of new products and applications. Shortcomings of laboratory user testing of mobile devices and applications can be overcome by using technology probes. Users can explore the technology in their real life setting without being directly observed by the evaluator. By using selfreporting materials (mainly diaries and photos) the user becomes the evaluator herself over a specified period of time (e.g. two or more weeks). Results of technology probes are typically qualitative. When conducting a usability evaluation using technology probes the functional prototype is given to the users. Technology probe studies last from days to weeks. The users note usability and usage problems (either in diary format, with photos or videos, using special probing material like post-cards, cards or simple post-its.) while using the product in their daily life. After the usage phase, interviews are conducted with

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the participants, to examine the gathered probing material. One of the main challenges of probe studies related to mobile applications is to motivate people to complete the probe material in mobile contexts. In order to address this shortcoming, other methods can be used, especially the experience sampling method (ESM). The main goal of experience sampling is to understand and gather information about people’s daily routines, their feelings and emotions. Typically participants are asked to write down what they are doing at a special period or point in the day. To adopt ESM for mobile usage participants get signals by chance (most commonly on their mobile phones). After they received a signal they either have to write down what they where currently doing (where, what context), or to answer some questions (typically also on the mobile phone). ESM allows to collect qualitative and quantitative data, and can be helpful in understanding typical usages and influencing factors for usability problems. The main qualities of experience sampling are that usability and user experience factors can be studied within a natural setting, in real time, on repeated time occasions and by request (Vermeeren & Kort, 2006). Conner, Barrett, Bliss-Moreau, Lebo and Kaschub (2003) provides additional information on ESM procedures and Van Esch-Bussemakers, Neerincx, Lindenberg and Streefkerk (2005) report experiences about the combination of ESM with classical usability evaluation methods. Future usability evaluations of mobile applications should also consider context-aware experience tools as well as the combination and triangulation of different in-situ methods (Intille, Rondoni, Kukla, Iacono & Bao, 2003).

applied in the field of software engineering, like automatic testing, formal descriptions of user and task models, model-based evaluations or critical incident and accident analysis. The authors are not aware of a broad usage of these methods in the field of mobile devices and applications, although one example in reference to Lee et al. (2005) was proposed in the SEM-CPU approach of a critical incident analysis. Usage of formal descriptions in multi-modal interfaces are more common, but are not typically used when evaluating multimodal interfaces for mobile devices. A model-based evaluation for MMI that might be also useful for evaluating multimodal mobile devices and services was described by Bernhaupt, Navarre, Palanque and Winckler (2008). Modelbased evaluation helps to inform traditional usability evaluation methods by providing information from the formal specification. Model-based evaluation might be helpful in the context of multimodal interfaces for mobile systems, as the evaluation of multimodal interfaces is difficult to realize with current usability methods. The different modalities can only be tested using a huge number of experiments (conducted in lab and field). With model-based evaluation the typical usability problems related to the multimodality could be found more easily. Model-based evaluation can be used in combination with any other usability evaluation method. When testing multimodal interfaces with a usability study, the following characteristics have to be taken into account: • •

Analytical Modeling In addition to the aforementioned user and expert centered usability evaluation methods and adopted methods for user inquiries we should not forget traditional usability evaluation methods originally

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•

Which pairs of (device, interaction technique) have to be tested? How can users address the system using the various communication channels and which channel can be used in the various contexts (tasks)? What types of fission and fusion can be tested (especially in the case of safety critical systems)?


•

How can the various dimension affecting usability evaluation of MMIs be addressed (Usage and interpretation of modalities, individual user preferences, context-of-use and activities supported by the system, etc.).

The goal of a usability test is typically to identify major usability problems within the interface. When conducing usability tests, the most frequently performed tasks (extracted from task analysis for instance) are commonly included in the usability test. Having a system that is described in a formal notation (unfortunately this is most often only the case for safety-critical systems), it is possible to identify not only the “minimum” number of scenarios to be tested but also to select more rationally the tasks that are to be focused on. This selection can be done using analysis techniques on the models that will help designer to identify complex or cumbersome behaviours that have been modelled and might require more specific usability testing. When testing multimodal interfaces, selection of scenarios reaches a higher level of complexity due to the significant number of possible combinations of input modalities. In order to test all (or most) of these combinations it is required to provide usability tests scenarios at a much lower level of description than what is usually done with systems featuring more classic interaction techniques. When setting up the usability test for multimodal interfaces the selection of tasks must be informed by the models. Tasks with high complexity in the low-level multimodal interaction must be listed exhaustively. Up to now, this comprehensive list has to be done by the expert describing the tasks to be evaluated. In addition, the frequency of highly-complex low-level interactions has to be estimated, based on the task-models. In the case study, the number of synergistic usages of speech and two mice has been counted. Based on this information the tasks for the usability test representing the low-level interaction are

selected. Additionally high-level tasks are selected to conduct the usability test. This way of selecting the tasks for the usability test, helps to represent all levels of multi-modal interaction. Thus the results of the usability test are more informative and connected to the precise design of the system. Model-based evaluation helps to support standard usability evaluation methods to overcome their reported weaknesses when testing multimodal interfaces. Provided with the adequate tools for editing models and generated marking graphs, the above descriptions might sound easy to follow. Of course the method will only show its benefits, when the method is carefully set up and conducted.

A CAsE sTUDy: EVAlUATING A TOURIsT GUIDE To exemplify how to combine various forms of usability evaluation within a product development cycle we want to use an example of a mobile tourist guide. The mobile tourist guide called “Sound of Music” enables tourists of the city of Salzburg to visit the original locations of the film scenes and to get information about that original location (sound, short videos, text). To determine the location a new form of GPS program enhancing pedestrian navigation (Kappacher, 2007). In the final version of the system users can access content either using the hierarchical menu or via pen-input. Output is given as sound feedback, visual feedback and includes also multimedia content. Currently there is no option to connect for additional information to the internet. Setting up a new tourist guide first an extensive literature review on touristic needs in that region was performed. A thorough user and task analysis including persona description was done. To investigate if the first (low fidelity) screen design would fit the user needs, if it enables the user to navigate and to find their way, and additionally

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it enables a thorough user experience, an expert analysis was conducted. The experts performed a heuristic evaluation in the real locations of the tourist guide (walking through the city), trying to understand how users would find their way through the city and use the information on site. This form of expert evaluation in the field was chosen as it allows performing the evaluation at an early stage of the development. Conducting the evaluation in the field improves additionally the findings of the experts (Po et al., 2004). The prototype was improved based on the findings of the expert analysis, including some extensions of functionality, especially a new way of triangulation to support navigation of pedestrians. Once a first version of the functional prototype is available, user studies can be performed. The lab allows more technical flexibility for the mock-up and it limits possible influencing factors of the trial situation (network problems in the field, by-passers in the street disturbing, difficult observation conditions, limited ability to mock-up a system for usage in the field). In the usability lab study it is easier to investigate how users will be using the new navigation system and how they will perceive the multimodal output. Input was limited (due to the used cell phone for the prototype stage) to navigation in a hierarchical menu with the navigation keys and numeric keys (T9). To investigate usability problems that might be influenced by contextual facts a field trial was used. Using an improved technical prototype, including most of the functionality of the final system, we conducted a usability test in the field. While walking to the typical locations of the Sound of Music Tour people can experience difficulties when using the local GPS system, to orientate themselves, while watching the content, or to simply try to look at the content together with other tourist (which might bring difficulties if someone else is starting to do some pen-interaction, while the other one uses the navigation of the mobile phone to select content).

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During the different evaluation phases we learned that problems initially appearing in the lab, where no problems in the real setting (especially tasks related to orientation and navigation), while problems that where found in the real world study, did not appear during the lab evaluation (the co-cooperation for input, the difficulties in listening to the content in a more noisy environment, the visibility of some design elements with very bright sunshine, the difficulties arising when a way – that was initially shown by the GPS – was blocked due to a festival). The combination of three evaluation methods helps to discover various forms of usability problems that a single method would not reveal. Especially the combination of lab and field user studies helps to understand what kind of usability problems users will face, once using the product in the real usage situation.

sUMMARy AND OUTlOOK This chapter presented an overview on currently used usability evaluation methods in the field of multimodal interaction techniques and in the field of mobile applications and services. It presents a methodological framework to describe different classes of usability evaluation methods and how these methods can be used to evaluate usability for mobile systems with multimodal interfaces. A listing of several methods available helps the reader to select an appropriate selection to test their development. Finally a short case-study description is given, to describe more detailed typical shortcomings and advantages of some of the methods and the influence of context on usability evaluation. The development pace of new forms of interaction techniques and new ways of mobile services is fast. From the methodological viewpoint only few studies investigate in detail the benefits and difficulties of the methods used to evaluate usability and how the combination of methods helps


to find more (or more efficient) usability problems. Especially in areas that go beyond any industrial set of mobile devices (pervasive computing, ubiquitous computing) and in areas that typically push the limits of technologies (games) or invent new forms of interaction techniques like exertion interfaces (Mueller, Agamanolis & Picard, 2003) evaluation of usability is still difficult due to a lack of suitable evaluation methods.

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KEy TERMs AND DEFINITIONs Context: Mobile services and devices can be used in various places and situations, by a single user or involving others. These circumstances are described as context of use or usage context. Experience Sampling Method (ESM): An in-situ method especially suitable for collecting quantitative and qualitative data with mobile and ubiquitous systems. ESM studies user experience factors in a natural setting, in real time, and over a longer period of time. In-Situ Evaluation Methods: Set of methods used to evaluate a system or prototype in its real usage context. Inspection-Oriented UEMs: Set of methods used by experts and most commonly based on guidelines to investigate possible usability problems. Usability Evaluation Methods (UEMs): A set of methods used to evaluate a system, mockup, or prototype in terms of usability. Usability Test: Performance measurements of users to determine whether usability goals have been achieved.

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About the Contributors

Patrizia Grifoni is a researcher at the National Research Council of Italy since 1990, where she works within the staff researchers of MultiModal Laboratory of the Institute of Research on Population and Social Policies. She received her degrees in electronics engineering at the University of Rome “La Sapienza”. From 1994 to 2000 she was professor of “Elaborazione digitale delle immagini” at the University of Macerata. She is author of more than 70 papers in international journals, books and conferences. She served as referee of several international conferences, books and journals and is co-organiser of international research workshops and events. Her scientific interests have evolved from query languages for statistical and geographic databases to human-computer interaction, multimodal interaction and languages, visual languages, visual interfaces, sketch-based interfaces, Web technologies, social networks. She was responsible for several projects funded by Italian and International Institutions. *** Simone Bacellar Leal Ferreira is an information systems scholar (graduate and post-graduate) at the Department of Applied Computer Sciences of the Federal University of the State of Rio de Janeiro (Universidade Federal do Estado do Rio de Janeiro - UNIRIO). She holds a Master’s degree (graphic computing) and a PhD (software engineering) in computer sciences from PUC-Rio. Her areas of expertise are usability, accessibility, human-computer interaction, information systems, color theory, entrepeneurship, eBusiness and environmental management. Barbara Rita Barricelli obtained a Bachelor’s degree in digital communication and a Master’s degree in information and communication technologies at the Department of Computer Science and Communication of Università degli Studi di Milano (Italy). Currently she is a PhD student of the Doctoral School for Computer Science at Università degli Studi di Milano. Her research topics are end-user development and specification, design and development of internationalized and localizable visual interactive systems. Teresa M.A. Basile got the Laurea degree in computer science at the University of Bari, Italy (2001). In March 2005 she obtained the PhD at the University of Bari defending a dissertation titled “A Multistrategy Framework for First-Order Rules Learning.” Since April 2005, she is a researcher at the Computer Science Department of the University of Bari. Her research interests concern the investigation of symbolic machine learning techniques. She is author of about 60 papers published on National and International journals and conferences/workshops proceedings and was/is involved in various National and European projects. Copyright © 2009, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited.


Regina Bernhaupt is currently on a sabbatical leave at the University of Toulouse III, IRIT-LIIHS to accomplish her habilitation. Since 2002 she is working as an assistant professor at the University of Salzburg on usability and user experience evaluation in non-traditional environments. Some of her recent publications include the usage of usability evaluation and user experience evaluation methods for non-traditional environments. She was involved in several (industrial) projects focusing on usability evaluation and user experience evaluation in environments like the home, public places, games or even human-robot interaction in industrial settings. She was organizing conferences like MobileHCI 2005, ACE 2007, Euroitv2008 and she acts as a reviewer for several journals and conferences. Michela Bertolotto received a PhD in computer science from the University of Genoa (Italy) in 1998. Subsequently she worked as a postdoctoral research associate in the National Center for Geographic Information and Analysis (NCGIA) at the University of Maine. Since 2000 she has been a faculty member at the School of Computer Science and Informatics of University College Dublin. Her research interests include Web-based and wireless GIS, spatio-temporal data modelling and 3D interfaces. Luca Bevilacqua, born in Naples 1965, got his graduate degree in electronic engineering in 1989 from the Naples University and his Master in Business Administration degree in 1992 from SDA of the Bocconi University in Milan. He has been working in big IT company since 1993 (Olivetti, Getronics, Sema, Atos Origin and Engineering) and since1996 he specialized in the field of R&D project, co financed by public bodies. He has been and still is scientific coordinator for several relevant R&D projects with cumulative budgets in excess of 20 mln €. Judy van Biljon is an associate-professor in the School of Computing at the University of South Africa. Starting her career as mathematics teacher she obtained a Master’s degree in computer science on the design of intelligent tutoring systems and a Doctoral degree on technology adoption in the field of human-computer interaction. Emilio Bugli Innocenti (1958) received MSc in physics at University of Florence in 1983. Over the last 20 years, he has been devoting his drive and enthusiasm to research and innovate information technology in different sectors ranging from software engineering to application fields such as health systems, automotive and mobile location-based services. He has been R&D director at large and medium-sized IT companies in Italy and lately he has been launching his own venture. Project manager of research projects at national and international level, he is author of several IEEE and ACM papers. Maria Chiara Caschera received her degree in computer science engineering at the University of Rome ‘La Sapienza’. She is actually a young research at CNR-IRPPS (National Research Council, Institute Of Research On Population And Social Policies). She is also a doctoral researcher in computer science at the ‘Roma Tre’ University sponsored by the MultiMedia & Modal Laboratory (M3L) of the National Research Council of Italy. She is mainly interested in human-computer interaction, multimodal interaction, visual languages, visual interfaces and sketch-based interfaces. Augusto Celentano is full professor of Information Systems at Università Ca’ Foscari Venezia, Italy. He received a Master’s Degree in electronic engineering from Politecnico di Milano. He has been deputy rector for information technology at Università Ca’ Foscari Venezia, member of scientific committees in

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research and educational centers of the Politecnico di Milano, and consultant and scientific coordinator of research projects of the European Union. His current research interests are in mobile, pervasive and context-aware information systems, in multimedia systems and in human computer interaction. Fang Chen joined NICTA in 2004 and is currently a senior principal researcher and the research group manager for the Making Sense of Data research theme at the ATP Laboratory. Dr. Chen has received conjoint professor and honorary associate positions with the University of New South Wales and the University of Sydney. Her main research interests is human machine interaction, especially in multimodal systems, ranging from speech processing, natural language dialogue, user interface design and evaluation, and cognitive load modelling. She has more than 80 publications in peer reviewed journals and conferences. She has also filed 19 patents in US, Australia, Europe, China, Japan, South Korea and Mexico. Karin Coninx gained a PhD in computer science following a study into human-computer interaction in immersive virtual environments. Since 1998 she has worked as a full-time professor at the Hasselt University and in that capacity lectures on various general computer science subjects and specialized subjects relating to man-machine interaction. As group leader of the human-computer interaction group of the Expertise Centre for Digital Media at the Hasselt University, she is responsible for various research projects relating to interaction in virtual environments, mobile and context-sensitive systems, interactive work spaces, user-centred development and the model-based realization of user interfaces. Fulvio Corno (PhD) is an associate professor at the Department of Computer Science and Automation of Politecnico di Torino. He received his MS and PhD degrees in electronics and computer science engineering from Politecnico di Torino in 1991 and 1995, respectively. He is (or was) involved in several European research projects (in the 4th, 5th and 6th Framework Programmes and in e-learning projects) as well as national and regional ones. His current research interests include the application of Semantic Web technologies to information systems engineering, the conception and design of intelligent domotic environments, and advanced multimedia and multi-modal interfaces for alternative access to computer systems. He published more than 150 papers at international conferences and 15 on international journals. Fabio Corvino was born in Caserta, Italy, in 1975. He received the engineer degree in electronic engineering at Second University of Naples in 2004, with a thesis in “Authentication and Authorization Mechanisms for Web Services”. He works in Research & Development department of Engineering.IT in Pozzuoli (Italy) and he has been working in multimodality on mobile devices since 2006. Danco Davcev obtained the degree of Engineer in electronics and computer science from the Faculty of Electrical Engineering at the University of Belgrade in 1972. He received the “Doctor – Ingenieur” degree in Computer Science from the University of ORSAY (Paris, France) in 1975 and PhD degree in Informatics from the University of Belgrade in 1981. From 1984 -1985 and 2001-2002 he was a Fulbright postdoctoral research scientist and visiting professor at the University of California, San Diego (USA). He is currently a full professor and head of the Computer Science Department at the University “Sts. Cyril & Methodius”, Skopje (Macedonia). He has more than 250 research papers presented on International Conferences or published in International Journals in Computer Science and Informatics.

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Alessia D’Andrea: she received her degree in Communication Science at the University of Rome ‘La Sapienza’. She is being a PhD student in multimedia communication at the University of Udine sponsored by the IRPPS of the National Research Council of Italy. She is mainly interested in communication science, social science, risk management, virtual communities, mobile technologies and health studies. Deborah Dahl is a consultant in speech and natural language understanding technologies, with over 25 years of experience. Her interests are in practical spoken and multimodal dialog systems and related standards. She serves as chair of the World Wide Web Consortium (W3C) Multimodal Interaction Working Group. Dr. Dahl is an editor of the EMMA standard, and contributed to the W3C standards for spoken dialog (VoiceXML) and speech grammars (SRGS). She has published over 50 technical papers, and is the editor of the book, Practical Spoken Dialog Systems. Dr. Dahl received her PhD in linguistics from the University of Minnesota in 1984. Joan De Boeck obtained an Engineering degree from the ‘Katholieke Hogeschool Kempen’ in 1996. He also obtained a Master degree in computer science (with an applied mathematics component) as an additional study in 1997. He started his part-time research career at the UHasselt in 1998 (while conducting several commercial projects elsewhere). He obtained his PhD in 2007. Now he is a post-doctoral researcher at the EDM and a teacher at the engineering department of the ‘Katholieke Hogeschool Kempen’. His research interests include virtual environments, human computer interaction, haptic interaction and multimodal interfaces. Kristine Deray is a senior lecturer in design at the University of Technology, Sydney. She has an interdisciplinary background of design, design computing, dance and movement studies and visualization//visual representation of the moving image. Together these contribute to her unique approach and focus on conceptual modelling of interaction, human centred design and computing, visualization, and designing for interaction, interactive spaces. She is a core member of the Centre for Digital Design (CDD) Research Group, and of the Contemporary Design Practice Research Group at UTS, with her specific focus being developing frameworks for explaining the mechanisms and (reciprocal) effects in interactions based on mappings derived from physical systems. Her research interests include human usability technologies for consumer /patient empowerment in health care and pervasive service industries. Kristine is a chief investigator on a governmental community health project with Health Services Network, (HsNET) focused on the social construction of knowledge and community participation in health services, derived from digital stories. Julie Doyle received a PhD in computer science from University College Dublin, Ireland. Her thesis focused on improving the usability and interactivity of mobile geospatial applications. Subsequently she worked as a postdoctoral fellow at the School of IT and Engineering, University of Ottawa, Canada, where she developed a framework to ensure the long term preservation of 3D digital data. Currently she is a postdoctoral fellow in HCI and Systems Engineering in the UCD School of Computer Science and Informatics. Her main research interests include the design of interfaces for mobile geospatial applications, multimodal HCI, usability and long term data preservation.

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Arianna D’Ulizia received her degree in computer science engineering at the University of Rome ‘La Sapienza’. She is actually young researcher at the Institute of Research on Population and Social Policies of the National Research Council of Italy. She is also doctoral researcher in computer science at the ‘Roma Tre’ University. She is the author of more than 20 papers on books and conferences. She is mainly interested in human computer interaction, multimodal interaction, visual languages, visual interfaces and geographical query languages. Castellina Emiliano (MS) is a PhD candidate at the Department of Computer Science and Automation of Politecnico di Torino. He received his MS degree in computer science engineering from the same institution in 2005.His current research interests include eye tracking (analysis and studies on gaze tracking algorithms), computer assistive technologies (development of special software applications for disabled people) and domotic systems. Benoît Encelle PhD in computer science (University Paul Sabatier – Toulouse III). I am an assistant professor in computer science at the IUT B of the Claude Bernard Lyon 1 University (France) and i do my research at the LIRIS research center. My main research interests are engineering for human-computer interaction (EHCI) and information system. More precisely, i work in the field of context-aware adaptation of content browsing. During and after my PhD, I contribute to the developments of three European projects in the field of accessibility (projects Contrapunctus, Lambda, Play2). Floriana Esposito, ECCAI Fellow, is since 1994 full professor of computer science at the University of Bari. Among her scientific interests there are similarity based learning, integration of numerical and symbolic methods in learning and classification, logical foundations of conceptual learning techniques, revision of logical theories by incremental learning, multistrategy learning, inductive logic programming. The major application fields are document processing and digital libraries and, recently, bioinformatics. She is author of more than 250 papers which published in international journals and Proceedings. She is in the PC of the most important scientific conferences on machine learning and artificial intelligence. Stefano Ferilli was born in 1972. He got a PhD in computer science at the University of Bari, and is currently an associate professor at the Computer Science Department of the University of Bari. His research interests include logic and algebraic foundations of machine learning, inductive logic programming, multistrategy learning, knowledge representation, digital document processing and digital libraries. He participated in various national and European (ESPRIT e IST) projects on these topics, and is a (co-)author of more than 100 papers published on journals, books and proceedings of national and international workshops/conferences. Fernando Ferri: he received the degrees in electronics engineering and the PhD in medical informatics. He is actually senior researcher at the National Research Council of Italy. From 1993 to 2000 he was professor of “Sistemi di Elaborazione” at the University of Macerata. He is the author of more than 100 papers in international journals, books and conferences. His main methodological areas of interest are: human-computer interaction visual languages, visual interfaces, sketch-based interfaces, multimodal interfaces, data and knowledge bases, geographic information systems and virtual communities. He has been responsible of several national and international research projects.

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Leopoldina Fortunati: since 20/02/2002 was the Italian representative at the COST Technical Committee on Social Sciences and Humanities and then since 24 March 2006 at the COST Domain Committee INSCH (Individuals, Societies, Cultures and Health), where she was elected as a member of the Executive group. She was part of the European funded research project SIGIS “Strategies of Inclusion: Gender and the Information Society”, August 1 2002- 31 January 2004, where she did 9 case studies on the social construction of femininity and masculinity in relation to the acquisition and use of the information technologies. She had an Outstanding Research Award from Rutgers University in 2001. For 15 years she was in the Advisory Board of the journal New Media & Society, she is an associate editor of the journal The Information Society and she serves as reviewer several journals. She founded, with others, in June 2003, The society for the social study of mobile communication (SSSMC – www. sociomobile.org) of which she is co-chair. Francesco Gaudino earned a Master’s degree in physics from University of Naples Federico II in 1999 (specialization in cybernetics and artificial intelligence), with a final dissertation about hypermedia knowledge acquisition and BDI agent for navigation assistance. He has been studying multimodality applications on mobile devices for the last two years. He works in Research & Development department of Engineering.IT in Pozzuoli (Italy). Marcos Gurgel do Amaral Leal Ferreira is a freelance Web designer with over ten years experience catering to a wide range of companies and professionals, from actors and record labels to law firms and private clinics. His work employs simplicity and objectivity to create functional and easy-to-navigate Web sites. His current interests are usability, color theory, Web standards and programming for Web applications. Giovanni Frattini received his Laurea degree in physics from University of Naples in 1995. He has worked for several companies with different roles. From 2000 his focus is on value added services. He has been delivery unit manager of Atos Origin Italia from 2002 to 2004. Currently he is chief architect of several projects providing is contribution to operational and research team. As researcher is main focus is currently on multimodal mobile services. Tiziana Guzzo: she received her degree in sociology and the PhD in theory and social research at the University of Rome “La Sapienza”. She is actually young researcher at the National Research Council of Italy. From 2005 to 2008 she was professor of Sociology at the University of Catanzaro. She is mainly interested in social science, new technologies, communication, mobile technologies, social networks, risk management, environment and tourism. Nadine JESSEL, PHD in computer sciences. I am an assistant professor in the teacher training centre in Toulouse. (IUFM Midi pyrénées). My research centre is the IRIT (Research Institute in computer science of Toulouse) and my research concerns the representation of literature, scientific and musical documents for their accessibility by a blind person. At the moment, I focus my research on graphical (musical and mathematical) documents, and their accessibility by a blind user with the help of Braille editors or using haptic devices. I am involved in European projects LAMDBA project (http://www. lambdaproject.org) and Contrapunctus project (http://www.punctus.org)

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Claudio De Lazzari (1956) received his laurea degree in Electronic Engineering (Automatic Controls) from the University of Rome “La Sapienza”. He is currently a Researcher of Italian National Council for Research (CNR) at the Institute of Clinical Physiology in Rome (IFC). His main scientific interests include: modelling of the cardiovascular system and different mechanical circulatory assist devices, simulation and optimisation of ventilatory assistance and ontologies in medicine. He is author of over 110 scientific papers published in international journals and conferences. He is involved in several scientific committee of international congresses and journals (on computer science in medicine). He recently edited the books “Modelling Cardiovascular System and Mechanical Circulatory Support”, published by Italian National Research Council. Massimo Magaldi received his degree in computer and automation engineering from University of Naples in 1998. From 1998 to 2000 he worked for Cap Genimi Italy, where he was involved in software systems for the telecommunication industries. In 2000 he joined Sema Group Italy (now Engineering IT), where he was involved in several telecom projects with different roles. Since 2003 he is working in the R&D area of the Telecom division. His research interests are in WFMS, BRMS, SOA, WS, Semantic and security. He is currently involved in a research project aiming to improve e-government efficiency leveraging Semantic grid Computing. Andrea Marcante, PhD in information society, has worked since 2003 with the Computer Semiotics Laboratory at the Dept. of Informatics and Communication of the University of Milano. His research focuses on human-computer interaction, and in particular on the issues related to multimodal and multimedia interaction with digital documents accessible via Web. He is a teaching assistant in fundamentals of digital communication and HCI in the digital communication course and in computer science course at the University of Study of Milano. He is also a Web consultant for publishing companies. Nicola Di Mauro got the Laurea degree in computer science at the University of Bari, Italy. In March 2005 he discussed a PhD thesis in computer science at the University of Bari titled “First Order Incremental Theory Refinement” facing the problem of incremental learning in ILP. Since January 2005, he is an assistant professor at the Department of Computer Science, University of Bari. His research activities concern relational learning. He is author of about 60 papers published on international and national journals and conference proceedings. He took part to European and national projects. Christian Müller-Tomfelde is a researcher at the CSIRO ICT Centre in Sydney. His expertise lies in the research area of human computer interaction and virtual and hybrid environments focusing on the support of co-located and remote collaboration. His interests also include novel forms of interaction, multimodal interaction such as sound feedback. He studied electrical engineering at the University of Hamburg-Harburg and worked at the Center for Art and Media Technology (ZKM) in Karlsruhe (1994). In 1997 he joined the GMD-IPSI’s division AMBIENTE “Workspaces of the Future” in Darmstadt/ Germany. He was involved in the i-LAND project and in the design and development of the Roomware components. Piero Mussio is full professor of computer science at the University of Milano (Italy), where he is the head of the Computer Semiotic Laboratory. His research interest include human computer interaction, visual communication and interaction design. He published more than 150 scientific papers in

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international journal and conference proceedings. He served as scientific coordinator and principal investigator of national, regional and local research units in several international and national projects and industrial contracts . He is Fellow IAPR, member of the ACM, member of the Pictorial Computing Laboratory; associate editor of Journal of Visual Languages and Computing. Marco Padula, graduated in mathematics at the State University of Milan; is a senior researcher in information technology at the ITC - CNR, Milan and professor of system usability design at the Catholic University of Brescia; took part in the on line management of the national earthquake catalogues for the National Group for the Defence from the Earthquakes, in national projects on document indexing for on line management, in the EU projects Aquarelle: A system for on line exploitation of cultural heritage, C-Web: A generic platform supporting community-webs, CONNIE (CONstruction News and Information Electronically. Stefano Paolozzi: he received his degree in informatic engineering at the University of “ROMA TRE”. He is a PhD student in computer science at the “ROMA TRE” University under supervision of Prof. Paolo Atzeni. His research activity with Multi Media & Modal Laboratory (M3L) of the IRPPSCNR of Italy is sponsored by a fellowship (assegno di ricerca). He is mainly interested in model management, multimodal interaction, multimedia database, temporal database, ontology alignement, ontology versioning, NLP. Loredana Parasiliti Provenza holds a post-doc position at the department of Computer Science and Communication (DICO) of the University of Milan (Italy). She received the Laurea degree in mathematics with full marks from the University of Messina (Italy) in 2001 and the Master’s degree in information and communication security from the University of Milan in 2002. In 2006 she received her PhD in computer science at the University of Milan. Her current research interests include specification, design and development of visual interactive systems, theory of visual languages, W3C technologies, XML security and privacy preserving data mining. Paolo Pellegrino is a postdoctoral researcher at the Department of Computer Science and Automation of Politecnico di Torino. He received his MS and PhD degrees in computer science engineering from Politecnico di Torino in 2004 and 2008, respectively. He has taken part in European, national and regional projects. His current research interests focus on knowledge representation systems, humancomputer interaction and intelligent environmental control. He has published papers at international conferences and a book chapter in an international journal. Pierpaolo Petriccione received a MS degree in telecommunications engineering at University of Naples Federico II (2003) and a Master’s degree in “software technologies” at the University of Sannio, Benevento (2004). From 2004 to 2007 he worked at the research department of Atos Origin Italia. Since 2007, he has been working at Engineering.IT and his main research interests are in the multimodality on mobile devices, content provisioning and GRID. Stefania Pierno got her graduate degree in mathematics at the “Federico II” University of Naples, in March 1993 with a final dissertation about stochastic processes. In 1996 he attended the Doctor’s degree from the High Mathematics National Institute (INDAM) of the “La Sapienza” University in Rome. She

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has been working in big IT company since 1997 (IBM Research Centre, Selfin, Sema, and Atos Origin). She currently works for Engineering in Research & Development department as technical director. Her research interests include innovative architecture as service oriented architecture and grid computing. Fabio Pittarello is a researcher at the Computer Science Department of Università Ca’ Foscari Venezia, Italy, where he teaches Web design and human computer interaction for the computer science curriculum. His current research interests include both theoretical and application oriented aspects of HCI in Web-based and ubiquitous contexts, with a particular reference to interaction in 3D environments, adaptivity, multimodality and universal access to information. Domenico M. Pisanelli (1959) graduated in electronic engineering (computer science in medicine) at the University of Rome in 1986. Since 1987 he is researcher at CNR (National Research Council). His main research interest is in the field of medical informatics (expert systems in medicine, computerized guidelines, and ontologies in medicine). From 1990 to 2001 he held an appointment at University of L’Aquila for teaching computer science in the Engineering Faculty, since 2004 he teaches “biolanguages and bioarchives” at Milan Polytechnic University. He is involved in several national and international projects on computer science in medicine and in European working groups on standardization in health care. He acted as project evaluator and reviewer in the UE e-Health sector. He recently edited the books “Ontologies in Medicine”, published by IOS-Press and “New Trends in Software Methodologies, Tools and Techniques” (the latter with Hamido Fujita). Chris Raymaekers is an assistant professor in computer science. His research is situated in the domain of human-computer interaction, more specifically interaction in virtual environments and usability. He obtained his PhD in 2002. He supports Prof. Dr. K. Coninx in managing the HumanComputer Interaction (HCI) group of EDM, where he concentrates on technical management of the research with regard to HCI in Virtual Environments. In this capacity, he aids in the management of several research projects (with national and European funding). His research interests include virtual environments, human-computer interaction, mixed reality, tangible and multi-modal interfaces, haptic interaction and haptic rendering. Karen Renaud is a computer scientist, and lectures in the Computing Science Department at the University of Glasgow. She has a keen interest in mobile phone usage and in developing an understanding of, and solutions for, older technology users. Renaud was previously a professor at the University of South Africa and the work reported in this chapter is the result of a continuing collaboration with Judy van Biljon. Ricardo Rodrigues Nunes is a professor (graduate and post-graduate) of digital design at Infnet Institute (Rio de Janeiro - Brazil). He is attending the Master’s Program of post-graduate in Information Systems at the Federal University of the State of Rio de Janeiro (Universidade Federal do Estado do Rio de Janeiro - UNIRIO). His areas of expertise are groupware, CSCW (computer supported cooperative work), virtual worlds, usability, accessibility, human-computer interaction, multimedia, information systems and e-learning.

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Luigi Romano received his Laurea degree in physics from University of Naples in 1996. In 1997 he attended the Pre-Doctoral School in Communication Systems at the Swiss Federal Institute of Technology Lausanne (EPFL). From 1997 on he was engaged in different roles for several telecommunication operators and in 2000 he started working in the system integration area of Sema Group. In the 2003 he was involved in a research project, in Engineering IT, attempting to ground mobile value added services in innovative architectures. He followed other research projects related to multimodal systems and artificial intelligence and he is currently involved in a project aiming to explore the potential of Semantic Grid Computing in e-government applications. Roberto Russo was born in 1974, degree in computer science at Federico II University of Naples. Since 1999 he has been working for Engineering.IT (ex Atos Origin Italia) with different roles. Currently he is team leader of several projects; his research interests include human-computer interaction, mobile application and architectures. He has been involved in several research projects such as SERVICEWARE and WISE and was author of several papers. Paolo Luigi Scala obtained a Bachelor’s degree in information technology at the Università degli Studi di Milano (Italy) in 2005, and is currently studying for his Master’s degree in information and communication technologies. He is a research cooperator at the ITC - CNR, Milan working as a software developer. His areas of interest are software I18N and L10N, industrial processes automation and geographic information systems. Vladimiro Scotto di Carlo earned a degree in physics (astrophysics) summa cum laude in 1998 at the Naples University Federico II. He has been working in multimodality on mobile devices since 2005. He works in Research & Development department of Engineering.IT in Pozzuoli (Italy). He lives in Naples with his wife and a son. Florence Sèdes is full professor in computer sciences, University of Toulouse. Her main research topics are about multimedia documents and semi-structured data. She is responsible in the IRIT (Toulouse Informatics Research Institute) of a team working on Information Systems and databases modelling and querying. She chairs the national research network about information, interaction, intelligence, GDR I3. Denis Silveira is MSc in informatics from Federal University of Rio de Janeiro (IM/UFRJ) and PhD student in production engineering from Federal University of Rio de Janeiro (COPPE/UFRJ), developing a method and tool for the elaboration and validation of business process using OCL. His research interests include requirements engineering, precise software modeling and web accessible. He is assistant professor at the Computer Science Department, Catholic University of Rio de Janeiro. His professional activities also include software development consultancy for companies in Brazil. Simeon Simoff is a professor of information technology and head of the School of Computing and Mathematics, University of Western Sydney. He is also an adjunct professor at the University of Technology, Sydney (UTS). Prior to this, he was a professor of information technology at the Faculty of Information Technology, UTS, where he established the e-Markets research program, currently running between UWS, UTS and UNSW (e-markets.org.au). He is also co-director of the Institute of Analytics

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Professionals of Australia and Series Editor of the ACS Publication Series “Conferences in Research and Practice in Information Technology”. In 2000–2005 he was the associate editor (Australia) of the ASCE International Journal of Computing in Civil Engineering. He is founder and chair of the ACM SIGKDD Multimedia Data Mining workshop series MDM@KDD, the Australasian Data Mining Conference series AusDM, and the Visual Data Mining international workshop series at ECML/PKDD and ICDM events. His research interests include data mining and visual analytics, design computing, human-centred computing and normative virtual worlds. Marcus Specht is professor for advanced learning technologies at the Open University of the Netherlands and is currently involved in several national and international research projects on competence based life long learning, personalized information support and contextualized learning. He received his Diploma in psychology in 1995 and a dissertation from the University of Trier in 1998 on adaptive information technology. From 1998 until 2001 he worked as senior researcher at the GMD research center on HCI and mobile information technology. From 2001 he headed the department “Mobile Knowledge” at the Fraunhofer Institute for Applied Information Technology (FIT). From 2005 he was associated professor at the Open University of the Netherlands and working on competence based education, learning content engineering and management, and personalization for learning. Currently he is working on mobile and contextualized learning technologies, learning network services, and social and immersive media for learning. After years of experience in IT industry, Yong Sun received his Master’s degree in multimedia from the University of Sydney. As a PhD student in the University of Sydney, he is participating enhanced PhD program jointly provided by National ICT Australia and the University of Sydney for a more practical research outcome. His dissertation research is focused on the development of a robust, flexible and portable multimodal input fusion technique that can adapt to other types of input modalities, to different types of applications and to multimodal constructions of different levels of complexity. His research interests are in designing applicable human-computer interaction technologies. Gianluca Supino was born in Caserta, Italy, on January the 7th 1976. He graduated in electronic engineering at University of Naples Federico II in 2003, with a final dissertation about electromagnetic sensors for no destructive evaluations on conducting materials. In 2004 he received a Master’s degree in software technologies. He currently works for Engineering.IT in Pozzuoli (Naples) and is employed in Research & Development department. He has been studying multimodality applications on mobile devices for the last two years, especially focusing on natural language processing. Sladjana Tesanovic has obtained her Bachelor’s degree at the Faculty of Electrical Engineering and Information Technologies, University “Ss. Cyril and Methodius” in Skopje, Macedonia. She is at the finishing phase of her Master studies at the same faculty. She works as a software developer in GISDATA. She develops applications related to geography information systems (GIS). Her favorite software environments and technologies are: VS.NET (Web, desktop and mobile), Oracle, SQL Server, ArcGIS Server. She has published several research papers and posters on International Conferences and International Journals. Her research interests are: distributed computer systems, ICT based collaboration systems, mobile services, artificial intelligence, image recognition.

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Vladimir Trajkovik has obtained his degrees at the Faculty of Electrical Engineering and Information Technologies, University “Ss. Cyril and Methodius” in Skopje, Macedonia. From February 1995 to December 1997 he worked for Macedonian Telecommunications, His current position is as professor at the Faculty of Electrical Engineering and Information technologies. During the past 8 years he published more than 60 research papers presented on International Conferences or published in International Journals in Computer Science and Informatics. In the same, period he coordinated and participated in more then 10 bilateral and multilateral EU projects. His research interests include: information systems analyses and design, object oriented systems, distributed computer systems, distance educational systems, multi-agent systems, ICT based collaboration systems and mobile services. Lode Vanacken obtained a Master’s degree in computer science at the tUL in 2005. He is currently working as a PhD student at the Expertise Centre for Digital Media (EDM), a research facility at the Hasselt University in Belgium, and is a member of the HCI Research group. His research interests are human-computer interaction and more specifically multi-modal interaction in virtual environments with interests into interaction techniques and model-based design of virtual environments. David C. Wilson is an assistant professor in the College of Computing and Informatics at the University of North Carolina at Charlotte. Dr. Wilson is an expert in intelligent software systems and a leading name in the field of case-based reasoning. Dr. Wilson’s research centers on the development of intelligent software systems to bridge the gaps between human information needs and the computational resources available to meet them. It involves the coordination of intelligent systems techniques (artificial intelligence, machine learning, etc.) with geographic, multimedia, database, internet, and communications systems in order to elicit, enhance, apply, and present relevant task-based knowledge. Norma Zanetti (1966) graduated in electronic engineering at Milan Polytechnic University in 1994. Since 1995 she is actively involved in international research projects co-funded by the European Commission, namely: Esprit, Tap, V VI and VII Framework Programme, eContentPlus. Since 2001, she has been involved, as NetXcalibur CTO, in research projects focusing on the implementation of mobile services in many application fields: intelligent transport systems (ITS), pervasive computing, eHealth, eGovernment and environment. Recently, she acted as project evaluator in the Ambient Assisted Living (AAL) sector and she was co-author of scientific papers on semantic-enabled technologies for esafety applications.

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Index

Symbols 3D virtual counterpart mapping 206 (MultiModal Manipulation for 3D Interaction framework (M3I) 26

A actual use (AU) 4 adapted multimodal end-user interfaces for XML-content 351 Adaptive Cruise Control (ACC) 233 Advanced Driver Assistance Systems (ADAS) 231, 232 AJAX (Asynchronous JavaScript and XML) 252 ALE LCMS 283 analytical modeling 450 ANDROME 158 annotation, a primitive operator 256 A-Posterior resolution methods,how integrate the interpretation methods 98 application definition 418 application prototype 157 architecture and communication standards 412 artificial intelligence-based fusion strategies 52 attributed relational graphs (AGRs) 91 AudioGPS 206 authoring-time content adaptation 352 autonomic Web processes 325

B back-end architectural overview 323 BANCO (Browsing Adaptive Network for Changing user Operativity) 251 bayesian network theory. 48

behavioral profile, inducing the 126 behavioural intention (BI) 5 BGP-MS 124 BioAPI Consortium 410 biometric standards 418 Brazil 265 Brazilian Accessibility Model was designed (e-MAG) 267 browsing content 351 business logic 308

C calibration distance 179 Call Control Markup Language (CCXML) 419 certification standards 411, 423 CHAT 293, 297 CoGenIVE (Code Generation for Interactive Virtual Environments) 155 Combinatory Categorial Grammar (CCG) 67 command buttons, use of for visually disabled 275 CoMPASS (Combining Mobile Personalised Applications with Spatial Services) 332 CoMPASS, multimodal interface 339 Computer-Aided Design (CAD) 246 computer input channels 36 computer output modalities 36 Computer Supported Cooperative Work (CSCW) 245 ConcurTaskTrees (CTT) 142, 155 Constraints Multiset Grammar (CMG) 47 content interchange formats (CIF) 285 content repository 311 content selection and structuring 113 contextual and semantic information 150


Index

contextual information, exploiting 151 CUBRICON 38 cultural heritage, Virtual Coal Mine Museum 158

D DAISY (Design Aid for Intelligent Support System) 247 data entry, for visually disabled 274 data fusion levels, in multimodal fusion 39 Datalog 127 DAVE_G (Dialogue-Assisted Visual Environment for Geoinformation) 345 decision-based approaches 91 decision-based fusion strategies 43 design methodology, adopted 235 Dialog Model 142, 155 DIGBE (Dynamic Interaction Generation for Building Environments) 247 disambiguating speech recognition Results with Gesture recognition results 80 discourse structure, from to intonation 105 DODEs (Domain Oriented Design Environments) 247 DOLCE 27, 235 DOPPELGANGER 124 driving simulator 159 dwell-time 173 dwell-time, and feedback time 182 dwell-time period 168

Excellence on End-User Development (EUD) 244 Extensible MultiModal Annotation (EMMA) 410 Extensible MultiModal Annotation (EMMA), representing user input 414 extensionNotification 414 external variables (EV) 3 eye tracker 198 eye tracking 190 eye tracking, comparing techniques 192

F FEDORA 311 filling out forms, for visually disabled 274 finite-state transducers (FSTs) 46 fission architecture 322 fission, defined 103 fission process 34 Flash 272 flow control, and interaction management 419 form, function and content, separation of 272 Form Interpretation Algorithm (FIA) 419 fusion 301 fusion, at the acquisition level 39 fusion, at the decision level 39 fusion, at the recognition level 39 fusion process 34 fusion results, utilizing the 62 fuzzy logic 51, 366, 368, 386

E

G

e-accessibility 266 early fusion 60 electronic whiteboard solutions 278 electro-oculography 192 Emergency Operations Centers (ECOs) 327 EMMA (Extensible MultiModal Annotation markup language) 298 Enabled Task Sets (ETS) 142 end-user requirements to the generation of adapted user interfaces 359 evolutionary methodology, for pervasive and multimodal interactive systems 243 Exact-Mobile 403

gaze-driven contextual information 188 gaze window 198 Geographic Information System (GIS) 339 geometric objects (GO) 211 Gestalt theory 106 GIS CoMPASS 331 GIS interfaces 331 global positioning systems (GPS) 59 GoBinder 2006 403 grammar generator 198 graphical user interfaces (GUIs) 35 GRID approach 315 gross domestic product (GDP) 231 GUIDE project 125

508

Index

H Hauptmann’s framework 25 Hidden Markov Model (HMM) 124 Hidden Markov model, set of parameters 90 Hidden Markov Models (HMMs) 41, 50, 51 human-computer interaction 34 human-computer interaction (HCI) 34, 167, 340 Human-Computer Interaction (HCI) 244 human input channels 36 human input modalities, and levels of interaction 21 human output modalities 36 human output modalities, six types of cooperation 37 human pointing, spatial organisation of 166 hybrid logic, subsumption in 74 hybrid multilevel approaches 91 hybrid multilevel approaches, how they deal syntactical ambiguities 97 hybrid multi-level fusion strategies 46

I identifying links and other elements, for visually disabled 273 IF-THEN rules. 52 image analysis 192 InkML 410, 417 inspection oriented methods 448 Instituto Benjamin Constant 265 integrating multimodality into an interface, potential limitations of 335 Intelligent Co-operative Systems 231 intelligent speed adaptation 230 Intelligent Speed Adaptation (ISA) 233 Intelligent Vehicle Safety Systems (eSafety systems) 231 Interaction CARE - Complementarity Assignment, Redundancy and Equivalence) framework 44 Interaction Locus (IL) 207 Interaction Manager (IM) 413 Interactive Field Trip System (IFTS) 283 intermediate prototypes 139 International Phonetic Alphabet (IPA) 421

Internet and Web accessibility, importance for those with visual impairment' 267 Internet Engineering Task Force’s (IETF’s) 309, 322 interpretation process, ambiguities connected to 92 iPods, useful for 392 IP signaling protocol (SIP) 297 IP signalling (SIP protocol) 296

J Java Applets 272 Java Speech API (JSAPI) 420, 423

L late fusion 60 Learning and Skills Development Agency (LSDA) 391 lexical ambiguities 95 LIDIM 318 Life Cycle Event Syntax, examples of 413 Location Based Services (LBS) 367 LTR (Left-to-Right) 259

M machine learning techniques 121 MADCOW (Multimedia Annotation of Digital Content Over the Web) 248 Magnetic Resonance Images (MRIs) 257 Management Information Systems (MIS) 2 mapping real objects 211 mapping services 331 mapping spaces 211 mapping the real world with a semantic virtual world\ 210 Match-Maker 324 MATIS (Multimodal Airline Travel Information System) 38 MATIS system 53 matrix to record parsing constituents 70 Media Resource Control Protocol Version 2, (MRCP v2) 415 Media Resources Control Protocol (MRCP) 410 mental context, of mobile handset usage model 7

509

Index

Meteorological Ontology 240 methods,deal classes of ambiguities 94 Microsoft Speech API (SAPI) 423 MMIF, future trends of 82 MMI,F related steps 61 MMIF, two challenges to 62 MMIF, types of 59 MMIF, unification-based 63 MMUIManager 299, 309 MMUIManager (MultiModal User Interface Manager) 298 mobile applications, usability evaluation of 444 Mobile Blackboard, implementation 381 mobile client, design of the 340 mobile devices 390, 443 Mobile ELDIT 404 mobile GIS, benefits of multimodal interaction for 334 mobile GIS interface design, benefits and challenges of multimodal interaction 333 mobile learning 389 mobile learning, advantages and limitations 394 mobile learning, collaborative approach 396 mobile learning, different contexts of us 398 mobile learning, for business 399 mobile learning, for education 399 mobile learning, for social life 400 mobile learning systems, and multimodal user interfaces 401 mobile learning systems, existing 403 Mobile Learning Systems (MLSs) 390 mobile learning, the next generation of learning 392 mobile MMI, user testing of 447 mobile multimodal applications, building 293 mobile multimodal systems 430 mobile phone adoption, by older users 1 Mobile Phone Technology Adoption Model (MOPTAM) 5 mobile synergic multimodality, system architecture for 298 mobile technology acceptance, specific models for 4 mobile terminals, must have feratures 295

510

Mobile Virtual Blackboard 367, 368 Mobile Virtual Blackboard, architecture 370 Mobile Virtual Blackboard, design 372 Mobile Virtual Blackboard, evaluation of' 383 Mobile Virtual Blackboard System 377 Mobile Virtual Table 386 MobiLP 403 modalities coordination 114 modality components, building new 422 Modality Components (MC’s) 412 modality selection 114 Model-based User Interface Design (MBUID) 141 Model-Based User Interface Design (MBUID) 140 modular ontology 230 MONA 297 MoSoSo (Mobile Social Software) 316 MRCP 417 multi-agent hidden markov model (M-AHMEM) 50, 51 multimedia communication, and user interface 368 multimodal agent-based user interface management 368 Multimodal Agent-Based user interface managment 378 multimodal architecture 412 multimodal architecture, overview 317 multimodal dialogue 21 multimodal fission 103 multimodal fission for mobile devices 115 multimodal fusion, finite-state based 65 multimodal fusion, mathematical methods in 48 multimodal HCI applications, pointing in 169 multimodal input fusion, introduction to 58 Multimodal Input Fusion (MMIF) 59 Multimodal Integrator (MI) 64 multimodal interaction, advantages of 20 multimodal interaction, from to multimodal fission 111 multimodal interaction, in the Semantic Web 25 multimodal interaction (MMI) 443 Multimodal Interaction (MMI) 412

Index

multimodal interaction systems 19 multimodal interaction, usability evaluation methods for 446 multimodal interfaces in mobile settings, evaluating 447 multimodality, importance of standards for 412 multimodal mobile virtual blackboard system 366 multimodal nterfaces for GIS that incorporate speech, issues in designing 336 Multimodal Presentation Markup Language 3D (MPML3D) 73 multimodal standards, requirements for 412 multimodal system architecture 22 multimodal systems 34 multimodal user interaction technology 58 multimodal user interface 423 multiple input modalities, fusion of 34 Multiple State-Time Delayed Neural Network (MS-TDNN) 53 Multi-State Mutual Information Network (MSMIN) 41 MUMIF system 47

N name ambiguity 200 Natural Language Parser (NLP) 64 Natural Language Understanding (NLU(' 302 navigational information, extracting and presenting 217 navigation, for visually disabled 273 navigation, indoor 221 navigation, outdoor 223 neural networks 53 NFR (non functional requirement) 270 NiMMiT 143, 154 NiMMiT (Notation for Multimodal Interaction Techniques) 144 NiMMiT primitives 145 NLU systems 303 nomadic user interfaces, towards 286 non-WIMP interfaces 167

O OGSA (Open Grid Service Architecture) 316 on-board system, and its ontologies 237

on-board vehichle multimodal interaction system 230 ontologies, role of 233 ontology, definition 234 ontology, what is an 234 ONTOTRAFFIC project 232 organization and hierarchization of elements, for visually disabled 273 Original Equipment Manufacturers (OEMs) 232 output process 322 output process, and fission 308 OWL 212, 236

P page, position of elements on a 271 pattern discovery strategies 121 perceived ease of use (PEU) 3, 4, 9 perceived usefulness (PU) 3, 4, 9 Personal Digital Assistant (PDA) 27 personalization systems, design guidelines for 354 Personal Program Guide system (PPG) 125 physical context, of usage environment 6 physical properties analysis 192 PLS 421 pocket PC 372 Pocket PC 383 pocket PCs 381 pointing 169 pointing action, observing a 175 pointing action, performing a 174 pointing actions, aspects of 171 pointing gesture, components of the 168 pointing, in the context of multimodal HCI 167 pointing, organisation of 173 pointing, spatial aspects of 172 pointing, temporal aspects of 171 Point Of Interest (POI) 289 points of interest (POI) 218, 334 Points of Interest (POI) 205 Polynomial Unification-based Multimodal Parsing Processor (PUMPP) 68 Presentation Model 143, 156 primitives, for user evaluation 147 Probabilistic Independence Networks (PINs) 41 511

Index

probes 147 profile, policy and their representations for XML (G2), concepts of 355 Pronunciation Language Specification (PLS) 411 Pronunciation Lexicon Specification (PLS) 421 Protégé-OWL editor 236 Provinciaal Galloromeins Museum (PGRM) 158 PUMPP, algorithm in the 70 PUMPP, architecture of the 69 PUMPP, basic principle in 69 PUMPP, disambiguating speech recognition results with 72 PUMPP, implementation of 73 PUMPP, parsing two parallel symbol strings with 77

Q QuickSet 38 QUICKSET 24 QuickTour 38

R radio frequency identification (RFID) 59 RAFT 278, 279, 280, 289 RAFT Field Trips 287 RAFT (Remotely Accessible Field Trips) 281 RAFT service frameworks 284 RAFT system 283 RDF (Resource Description Framework) 247 real semantic object (RSO) 211 recognition-based approaches 90 recognition-based approaches, how solve ambiguities due to the inaccuracy 94 recognition-based fusion strategies 40 representing interactions on mobile multimodal systems, specific design requirements for visual elements and displays for 438 requirements analysis 196 Resource Description Framework (RDF) 26 Resource Planning Support (RPS) 327 Right-to-Left (RTL) 259 Road and Traffic Security Ontology 240 role ambiguity 201

512

run-time adaptation 353 Runtime Framework (RF) 412

S screen readers 271 scripts 272 selection granularity 177 selection granularity, and calibration radius 182 self reporting and inquiry 449 semantic information, exploiting 152 Semantic Interpretation for Speech Recognition (SISR) 411, 420 Semantic MMIF, approaches to 63 semantics, describing 212 Semantic Web 25, 26 Semantic Web fields 19 Senior Technology Acceptance & Adoption Model (STAM) 10 Senior Technology Acceptance & Adoption Model (STAM), components of 10 Senior Technology Acceptance & Adoption Model (STAM), proposing 10 senior technology acceptance and adoption model (STAM) 1 Senior Technology Acceptance and Adoption Model (STAM) 11 sequence mining 121 sequentiality of code elements, barrier to visually disabled 271 Short Message Service (SMS) 403 Smart Objects 26 SMARTSUMO 27 SmartWEB 27 social context, of mobile phone use 7 Software Shaping Workshop (SSW) 244 Software Shaping Workshop (SSW) methodology 249 speech- and gaze- based inputs 188 Speech Application Language Tags (SALT) 418 Speech Applications Language Tags (SALT) 420 speech driven interaction 293 speech recognition 192 Speech Recognition Grammar Specification (SRGS) 420

Index

Speech Synthesis Markup Language (SSML) 411, 420 SSW, system's architecture 251 STAM (senior technology acceptance and adoption model) 1 startRequest 413 State Chart XML (SCXML) 411, 419 static profile, building the 126 statistical fusion strategies 48 SUMO 27 synergic mobile multimodal services, platform for 294

T TADEUS project 246 TAGUS 124 TAM (Technology Acceptance Model) 3 Task Model 155 tasks and connected personalization requirements (G1), identification of 354 technological context, of mobile phones 7 technology acceptance models 3 Technology Acceptance Model (TAM) 3 technology adoption models 12 technology adoption, processes 2 tele-conferencing, in a virtual room 158 Thin Client Approach 318 tools, role of 411 tool support 155 tourist guide, evaluating a 451 Travel Ontology 241 Typed Feature Structures (TFS) 63

U UMT 124 Unified Technology Acceptance Model (UTAUT) 3 Unified Theory of Acceptance and Use of Technology (UTAUT) 4 urban environments, semantic description of 214 Urban Tapestries 404 usability, by visually impaired 269 usability directives, focused on accessibility 270 usability evaluation methods (UEMs) 443

user interface 411 User Inter face Description Language (UIDL), XML based 143 User Ontology 241 UTAUT (Unified Theory of Acceptance and Use of Technology) 4

V vehicle data 232 Vehicle Infrastructure Integration (VII) 232 Vehicle Security Ontology 239 Venice 204 Venice, navigating through 220 VIENA system 27 virtual car sound (VCS) 159 virtual environment interfaces 139 virtual environment, model-based design of a 142 virtual environments (VEs) 139 virtual semantic object (VSO) 211 visual information, and fission 106 visualisa, interactions from a perspective of 432 visualization of interactions on mobile multimodal systems, design guidelines for 435 visually impaired-machine interaction 269 visually impaired persons 265 visual modality 421 visual representations of interactions 430 visual representations of interactions on mobile multimodal systems, high-level design requirements for 436 vocal unit 199 voice modality 419 voice search application 424 VR-DeMo process 158 VR Juggler 139

W W3C multimodal architecture 409 WAI (Web Accessibility Initiative) 266 Web Accessibility Initiative (WAI) 353 Web, accessibility to or e-accessability 266 WELCOME 403

513

Index

WIMP (Windows, Icons, Menu and Pointer) 208 WIMP (Windows Icons Menus Pointing) 167 Workflow Management Coalition (WfMC) 323 Workflow Management System (WFMS) 323

514

World Wide Web Consortium (W3C) 236 WWHT (What-Which-How-Then) 113

X XHTML+Voice (X+V) language 208 Xpointe 247

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